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Enhanced sparse RCNN for transmission line bolt defect detection via text-to-image data augmentation and quality filtering 认领 引用
1
作者 Chen Zhenyu Yan Huaguang +2 位作者 Du Jianguang Xue Meng Zhao Shuai 《High Technology Letters》 EI CAS 2026年第1期11-20,共10页
To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detecti... To address the issue of inconsistent image quality and data scarcity in bolt defect detection for transmission lines,this paper proposes an improved sparse region-based convolutional neural network(RCNN) based detection framework integrating image quality evaluation and text-to-image data augmentation.First,a HyperNetwork-based image quality assessment module is introduced to filter low-quality inspection images in terms of clarity and structural integrity,resulting in a high-quality training dataset.Second,a text-to-image diffusion model is utilized for sample augmentation.By designing text prompts that describe various bolt defect types under diverse lighting and viewing conditions,the model automatically generates realistic synthetic samples.The generated images are further filtered using a combination of quality and perceptual similarity metrics to ensure consistency with the real data distribution.Building upon the sparse RCNN baseline,a dynamic label assignment mechanism and a random decision path detection head are incorporated to enhance bounding box matching and prediction accuracy.Experimental results demonstrate that the proposed method significantly improves detection accuracy(mAP@0.5) over the original sparse RCNN while maintaining low computational cost,enabling more efficient and intelligent inspection of transmission line components. 展开更多
关键词 sparse region-based convolutional neural network HyperNetwork image quality assessment text-to-image generation data augmentation bolt defect detection transmission line inspection
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A Survey of Surface Defect Detection in Machine Vision:Addressing Core Challenges,Methodologies,and Dataset Analysis 认领 引用
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作者 Langyue Zhao Yubin Yuan Yiquan Wu 《Computers, Materials & Continua》 SCIE EI 2026年第8期125-164,共40页
This paper presents a systematic survey of machine vision-based surface defect detection technologies,focusing on five core challenges in the field:interference from complex backgrounds,small object detection,class im... This paper presents a systematic survey of machine vision-based surface defect detection technologies,focusing on five core challenges in the field:interference from complex backgrounds,small object detection,class imbalance,dynamic scene modeling,and cross-scenario generalization.It reviews key technical approaches corresponding to these challenges over the past five years.Furthermore,a dataset characterization analysis framework is established around these challenges,summarizing and comparing the characteristics of over 40 publicly available datasets across more than ten scenarios,including PCB,photovoltaic,metal,and pavement surfaces.Quantitative selection metrics(such as the small target coefficient and texture complexity)are proposed for challenges like small target detection and complex backgrounds,offering a methodological guide for aligning research questions with benchmark data.Finally,the paper summarizes current limitations and provides an outlook on new paradigms driven by large-scale models and the construction of high-quality benchmark datasets,aiming to offer valuable references for both research and engineering practices in this field. 展开更多
关键词 Surface defect detection machine vision complex scenarios dataset survey industrial vision
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Railway Track Defect Detection Based on Dynamic Multi-Modal Fusion and Challenging Object Enhanced Perception 认领 引用
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作者 Yaguan Wang Linlin Kou +3 位作者 Yang Gao Qiang Sun Yong Qin Genwang Peng 《Structural Durability & Health Monitoring》 EI 2026年第2期195-212,共18页
The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defec... The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset. 展开更多
关键词 Railway safety track defect detection multi-modal data object detection
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Attention-Enhanced YOLOv8-Seg with WGAN-GP-Based Generative Data Augmentation for High-Precision Surface Defect Detection on Coarsely Ground SiC Wafers 认领 引用
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作者 Chih-Yung Huang Hong-Ru Shi Min-Yan Xie 《Computers, Materials & Continua》 SCIE EI 2026年第5期1431-1455,共25页
Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects... Quality control plays a critical role in modern manufacturing.With the rapid development of electric vehicles,5G communications,and the semiconductor industry,high-speed and high-precision detection of surface defects on silicon carbide(SiC)wafers has become essential.This study developed an automated inspection framework for identifying surface defects on SiC wafers during the coarse grinding stage.Thecomplex machining textures on wafer surfaces hinder conventional machine vision models,often leading to misjudgment.To address this,deep learning algorithms were applied for defect classification.Because defects are rare and imbalanced across categories,data augmentation was performed using aWasserstein generative adversarial network with gradient penalty(WGAN-GP),along with conventionalmethods.An improved YOLOv8-seg instance segmentationmodel was then trained and tested on datasets with different augmentation strategies.Experimental results showed that,when trained withWGAN-GP–generated data,YOLOv8-seg achieved mean average precision values of 87.0%(bounding box)and 86.6%(segmentation mask).Compared with the traditional WGAN-GP,the proposed model reduced Frechet inception distance by 32.2%and multiscale structural similarity index by 29.8%,generating more realistic and diverse defect images.The proposed framework effectively improves defect detection accuracy under limited data conditions and shows strong potential for industrial applications. 展开更多
关键词 Data augmentation defect detection silicon carbide(SiC )wafer WGAN-GP YOLOv8-seg
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High-Performance and Lightweight Detection Network for Substation Equipment Defect Detection 认领 引用
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作者 Hongliang Tian Xiaoke Liu +1 位作者 Bolin Song Chenying Pei 《Computers, Materials & Continua》 SCIE EI 2026年第9期1238-1261,共24页
In the intelligent inspection of power systems,the detection of equipment defects is confronted with problems such as low background discrimination,multi-scale morphological differences,and the difficulty in identifyi... In the intelligent inspection of power systems,the detection of equipment defects is confronted with problems such as low background discrimination,multi-scale morphological differences,and the difficulty in identifying small targets and fine-grained defects,which makes it hard for existing models to balance detection accuracy and computational efficiency.To address this,this study proposes an improved lightweight detection framework,GRID-YOLO.This framework enhances the semantic discrimination ability of the backbone network for complex defects by introducing a cross-stage hierarchical multi-cognitive spatial attention module(C2MSA),designs an enhanced multi-scale bidirectional feature pyramid network(EMFPN)to achieve efficient alignment and preservation of cross-level features,and builds a hybrid attention reparameterization fusion module(HAFusion)to dynamically filter and enhance heterogeneous features.Additionally,a grouped distribution focal loss head(GDFLHead)is adopted to strengthen the collaborative optimization between classification and regression tasks.In this study,a dedicated dataset for substation equipment defect detection was constructed,and extensive comparative experiments were conducted.The results show that GRID-YOLO achieves an mAP50 of 78.3%,which is 6.1 percentage points higher than the baseline model.The mAP50–95,Recall,and F1-score reach 52.3%,73.6%,and 77.6%,respectively,and the inference speed reaches 506 FPS.Compared with various advanced detection models,GRID-YOLO demonstrates strong overall performance and can better meet the actual needs of substation equipment defect detection in the intelligent inspection scenarios of power systems. 展开更多
关键词 Substation equipment defect detection hybrid attention multi-scale feature fusion GRID-YOLO
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Research on Steel Surface Defect Detection Algorithm Based on YOLOv11-ODF 认领 引用
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作者 Zhengxiang Ma Xiaofei Ma +2 位作者 Xiaoliang Liu Heng Zhang Weichao Yu 《Structural Durability & Health Monitoring》 EI 2026年第4期417-436,共20页
Steel surface defect detection is a key technology for ensuring the quality of steel products and the automation of production.However,in actual industrial scenarios,the complex texture background of steel surfaces of... Steel surface defect detection is a key technology for ensuring the quality of steel products and the automation of production.However,in actual industrial scenarios,the complex texture background of steel surfaces often leads to lowrecognition of tiny defect features and easy confusion,and the full extraction and fusion ofmulti-scale features remain challenging.To address these issues,this paper proposes a lightweight and high-precision detection model based on the improved YOLOv11n,named YOLOv11-ODF.Firstly,in the backbone network,a C3k2_ODConv module integrating full-dimensional convolution(ODConv)is constructed,which enhances the model’s ability to capture subtle defect features through multi-dimensional dynamic weights,and combines the C2PSA attention mechanism to optimize the feature representation in both spatial and channel dimensions.Secondly,in the feature fusion network(Head),an OD_WT_Fuse module is designed to replace the traditional fusion method,effectively improving the efficiency of cross-scale information transmission and semantic consistency.In addition,an anisotropic strip spatial pyramid pooling(ASSPPF)module is designed to further expand the receptive field and enhance the robustness of detecting irregular multi-scale defects.Experimental results show that on the NEU-DET dataset,the mAP@0.5 of YOLOv11-ODF reaches 77.1%,significantly improving by 3.2% compared to the original YOLOv11 model;the precision and recall increase by 1.6% and 4.6%,respectively,significantly reducing the missed detection rate of tiny defects.While achieving significant performance improvements,the model parameters only increase by 0.9 M,achieving an excellent balance between detection accuracy and computational efficiency,providing an effective technical solution for high-quality real-time automatic detection in industrial environments. 展开更多
关键词 Steel surface defect detection YOLOv11n full-dimensional convolution feature fusion NEU-DET dataset
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A cascaded pipeline defect detection and size estimation method based on the YOLOv11 and physics-informed network using the MFL data 认领 引用 被引量：1
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作者 Xi-Ming Chen Meng-Kai Fu +1 位作者 Jia Shao Xiao-Ben Liu 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1505-1518,共14页
Long-distance oil and gas pipelines are crucial in the global energy network.However,due to complex internal and external environments,defects can be formed on a pipeline's surface,posing severe threats to structu... Long-distance oil and gas pipelines are crucial in the global energy network.However,due to complex internal and external environments,defects can be formed on a pipeline's surface,posing severe threats to structural safety.Aiming to detect surface defects,recent works have used magnetic flux leakage(MFL) inspection data for defect recognition and defect size estimation.Accurately locating and measuring defects based on the MFL data is essential for pipeline integrity assessment and safety maintenance.To obtain effective MFL data on pipeline defects,this study constructs an experimental pipeline at the Daxing pulling-through test site in Beijing.An ultra-high-definition MFL inspection robot is employed to collect defect data,which are then used to construct a defect detection and size estimation database.In addition,to achieve precise defect recognition and quantification,a cascaded method,which integrates a mature computer vision detection model,the YOLOv11 model,with a physics-informed and data-driven prior deep-learning quantification model,is proposed.Validation results show that,even for a limited amount of data,the proposed defect recognition model can achieve an AP50 of 92.1% at a confidence threshold of 0.6,a precision of 100%,a recall of 84.29%,and an F1-score of 91.47 %,indicating high accuracy in identifying surface defects on pipelines.The quantification model can achieve the goodness of fit(Gof) values of 0.987,0.979,and 0.994 for defect length,width,and depth,with the mean absolute percentage error(MAPE) of 7.97%,8.52%,and 4.74%,respectively.Comparison analysis with different models confirms the superiority of the proposed cascaded recognition and quantification approach.The results also demonstrate that the proposed method can effectively identify and quantify defects in long-distance pipelines.Finally,it can improve the interpretation efficiency of MFL inspection data and provide reliable support for residual strength assessment and remaining life prediction of pipelines. 展开更多
关键词 Oil and gas pipeline MFL inspection Defect detection Defect size estimation
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CSYOLO:a YOLOv8-based PCB defect detection model integrating composite backbone networks and dynamic snake convolution 认领 引用 被引量：2
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作者 LIU Chunjuan ZHANG Mingxuan +2 位作者 YAN Haowen WU Xiaosuo WANG Yixiang 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第1期151-161,共11页
An improved CSYOLOv8 model based on YOLOv8 model is developed specifically for identifying defects in printed circuit board(PCB).Firstly,a composite backbone network is designed to carry out additional feature extract... An improved CSYOLOv8 model based on YOLOv8 model is developed specifically for identifying defects in printed circuit board(PCB).Firstly,a composite backbone network is designed to carry out additional feature extraction,which enriches the expression ability of features and enhances the detection accuracy of the model.Secondly,a YOLO-FPN(Feature pyramid network)structure is designed to supplant the original neck network,which enhances the feature fusion ability of the model and improves the detection accuracy of small target objects.Furthermore,to enhance the model’s capability to extract tubular features,dynamic snake convolution is implemented.Finally,MPDIoU loss function is employed to enhance both the convergence rate and the precision of the model.Experiments show that the mAP of the improved model on the PCB defect dataset reaches 96.6%,which is 4.5%higher than that of the YOLOv8 model,and the number of parameters is only 3256862,and the average detection speed is 51.8 frames per second,which meets the requirements of detection accuracy and efficiency. 展开更多
关键词 printed circuit board(PCB) deep learning defect detection YOLOv8 multi-scale feature fusion loss function
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Lightweight Small Defect Detection with YOLOv8 Using Cascaded Multi-Receptive Fields and Enhanced Detection Heads 认领 引用
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作者 Shengran Zhao Zhensong Li +2 位作者 Xiaotan Wei Yutong Wang Kai Zhao 《Computers, Materials & Continua》 SCIE EI 2026年第1期1278-1291,共14页
In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds... In printed circuit board(PCB)manufacturing,surface defects can significantly affect product quality.To address the performance degradation,high false detection rates,and missed detections caused by complex backgrounds in current intelligent inspection algorithms,this paper proposes CG-YOLOv8,a lightweight and improved model based on YOLOv8n for PCB surface defect detection.The proposed method optimizes the network architecture and compresses parameters to reduce model complexity while maintaining high detection accuracy,thereby enhancing the capability of identifying diverse defects under complex conditions.Specifically,a cascaded multi-receptive field(CMRF)module is adopted to replace the SPPF module in the backbone to improve feature perception,and an inverted residual mobile block(IRMB)is integrated into the C2f module to further enhance performance.Additionally,conventional convolution layers are replaced with GSConv to reduce computational cost,and a lightweight Convolutional Block Attention Module based Convolution(CBAMConv)module is introduced after Grouped Spatial Convolution(GSConv)to preserve accuracy through attention mechanisms.The detection head is also optimized by removing medium and large-scale detection layers,thereby enhancing the model’s ability to detect small-scale defects and further reducing complexity.Experimental results show that,compared to the original YOLOv8n,the proposed CG-YOLOv8 reduces parameter count by 53.9%,improves mAP@0.5 by 2.2%,and increases precision and recall by 2.0%and 1.8%,respectively.These improvements demonstrate that CG-YOLOv8 offers an efficient and lightweight solution for PCB surface defect detection. 展开更多
关键词 YOLOv8n PCB surface defect detection lightweight model small object detection
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A Multimodal Defect Detection Method for Key Components of Rail Transit Systems 认领 引用
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作者 Haoyu Li Jiayi Wang +5 位作者 Zhaoyu Wu Shuo Yan Ziqi Zhang Yang Gao Genwang Peng Zhiwei Cao 《Structural Durability & Health Monitoring》 EI 2026年第3期329-347,共19页
Key components of rail transit systems,such as tracks and vehicle bodies,are prone to developing various types and manifestations of defects during long-term operation.These defects not only accelerate component aging... Key components of rail transit systems,such as tracks and vehicle bodies,are prone to developing various types and manifestations of defects during long-term operation.These defects not only accelerate component aging and failure but also pose serious threats to train operational safety.Among existing intelligent detection methods,they mostly rely solely on visible light images demonstrate limited robustness in complex scenarios.This limitation stems from their high dependence on ambient lighting conditions,rendering them insufficient to meet practical railway inspection requirements.While mainstream multimodal detection methods incorporate the complementary strengths of heterogeneous data sources,they fail to fully leverage the intrinsic associative value of data across different modalities.Furthermore,the occurrence frequency and sample size of various rail transit defects exhibit significant disparities,resulting in severe sample imbalance across detection datasets.This substantially reduces the detection accuracy for rare defect categories.To address these critical technical challenges,this paper proposes a multimodal defect detection method for key components of rail transit systems.The method introduces a multimodal input architecture by integrating Red,Green,Blue(RGB)visual data with depth geometric data.It incorporates a self-learning deep feature fusion module that enables complementary enhancement and deep coupling of heterogeneous modal information.This is achieved through targeted feature extraction and multi-round interactive fusion across different modalities.Additionally,we propose a weighted composite balanced loss function that employs dynamic adaptive weighting factors to adjust the model optimization direction in real time.This method effectively mitigates training bias caused by sample imbalance and improves detection performance for minority defect classes.Finally,experimental results on the track fastener defect RGBD dataset and the vehicle body fastener defect RGBD dataset demonstrate that the proposed method achieves optimal defect detection accuracy and meets real-time inspection requirements. 展开更多
关键词 Track safety track inspection defect detection multimodal data object detection
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Defect Detection of Wind Turbine Blades Using Multiscale Feature Extraction and Attention Mechanism 认领 引用
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作者 Yajuan Lu Yongtao Hu +2 位作者 Jie Li Jinping Zhang Jingjing Si 《Structural Durability & Health Monitoring》 EI 2026年第2期383-417,共35页
To address challenges in wind turbine blade defect detection models,primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices,this study proposes a... To address challenges in wind turbine blade defect detection models,primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices,this study proposes a wind turbine blade defect detection model,WtCS-YOLO11,that incorporates multiscale feature extraction and an attention mechanism.Firstly,the cross-stage partial with two kernels and a wavelet convolution module(C3k2_WTConv)is proposed by introducing wavelet convolution into the module.The cross-stage partial with two kernels(C3k2)module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field,enable multiscale feature extraction,and reduce computational parameter usage.Second,the convolutional block attention module(CBAM)is proposed and applied to the neck network,integrating channel and spatial attention,allowing the model to focus on essential features and enhance its ability to detect large targets.In addition,the model employs shape-aware intersection over union(Shape-IoU),which focuses on the shape and scale of bounding boxes,and combines the normalized Wasserstein distance(NWD)to calculate bounding box similarity,thereby improving the accuracy of bounding-box regression.In this study,a dataset for wind turbine blade defect detection was constructed covering six defect categories.The experimental results showed that the precision(P),recall(R),and mean average precision at the intersection over union threshold of 0.5(mAP50)for the WtCS-YOLO11 model were 84.4%,86.9%,and 89.7%,respectively.Compared to the baseline You Only Look Once 11(YOLO11)model,P,R,and mAP50 improved by 5.9%,2.5%,and 2.4%,respectively,with virtually no increase in computational complexity or parameter count.WtCS-YOLO11 improved the precision measurement accuracy.Its model size and computational complexity are suitable for deployment on edge devices,and it achieves high inference speed,meeting the application requirements for real-time wind turbine blade defect detection. 展开更多
关键词 Wind turbine blade defect detection wavelet convolution YOLO11 object detection
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An Efficient DETR-Based Framework for Small Target-Aware Industrial Surface Defect Detection 认领 引用
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作者 Yuting Wang Bingyang Guo +1 位作者 Jianing Duan Ruiyun Yu 《Computers, Materials & Continua》 SCIE EI 2026年第9期2283-2304,共22页
Industrial surface defect detection requires accurate localization of small and weak-boundary defects under tight runtime constraints for on-line inspection.This paper presents an efficient DETR-style defect detector ... Industrial surface defect detection requires accurate localization of small and weak-boundary defects under tight runtime constraints for on-line inspection.This paper presents an efficient DETR-style defect detector with three components.First,we build a hybrid feature extractor by coupling a ConvNeXt-T backbone with a lightweight Feature Pyramid Network(FPN)to strengthen multi-scale representations for small and subtle defects,thereby improving detection performance in challenging industrial environments.Second,to address the high computational cost of original DETR,we adopt multi-scale deformable attention to replace the quadratic-cost global self-attention mechanism,substantially improving efficiency.Third,to improve per-class robustness on hard defect categories with negligible overhead,we incorporate a class-reweighted focal loss(focal loss with no-object down-weighting together with effective-number reweighting)for classification.Experiments on NEU-DET show that our method achieves 85.0%mAP@0.5 and 47.5%mAP@[0.5:0.95],improving over the original DETR baseline(81.0%mAP@0.5).Under the same runtime setup(NVIDIA 3090,FP16,batch size 1,512×512),latency is reduced from 71.4 to 53.3 ms with 18.8 img/s throughput.These results demonstrate that our framework achieves a superior accuracy-efficiency trade-off for near real-time on-line inspection(approximately 18.8 img/s,53.3 ms latency)in medium-speed large-scale industrial manufacturing scenarios. 展开更多
关键词 Detection transformer surface defect detection feature pyramid network industrial defects
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SIM-Net:A Multi-Scale Attention-Guided Deep Learning Framework for High-Precision PCB Defect Detection 认领 引用
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作者 Ping Fang Mengjun Tong 《Computers, Materials & Continua》 SCIE EI 2026年第4期1754-1770,共17页
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ... Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection. 展开更多
关键词 Deep learning small object detection PCB defect detection attention mechanism multi-scale fusion network
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From Public Benchmarks to a Low-Resource Target Domain:A Comparative Study of Wood Surface Defect Detection 认领 引用
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作者 Khanh Nguyen-Trong Tan Nguyen-Thi-Thanh 《Computers, Materials & Continua》 SCIE EI 2026年第9期1811-1829,共19页
Automated wood surface defect detection is difficult to evaluate reliably because defects are often small,low-contrast,and visually confounded by natural wood texture,while reported performance can vary substantially ... Automated wood surface defect detection is difficult to evaluate reliably because defects are often small,low-contrast,and visually confounded by natural wood texture,while reported performance can vary substantially with benchmark design and domain shift.To address this issue,we conduct a comparative study across three practically relevant settings:a curated seven-class benchmark,a broader in-domain seven-class protocol derived from the same source dataset,and supervised adaptation to a low-resource Vietnamese target domain.We compare lightweight two-stage detectors based on Faster Region-based Convolutional Neural Network(Faster R-CNN)with MobileNetV3-FPN against a compact You Only Look Once version 8(YOLOv8s)baseline,while also testing two small-object-oriented YOLO refinements as targeted diagnostic variants rather than as the primary claimed contribution.Across in-domain experiments,the compact YOLOv8s baseline delivers the strongest performance,achieving 84.38%AP50 on the curated benchmark,whereas performance drops to 81.16%AP50 under the broader protocol,indicating that benchmark breadth materially changes the apparent difficulty of the task and the relative strength of competing models.In the target-domain setting,source-initialized fine-tuning improves optimization behavior and can outperform target-only training in a representative single run,but repeated-seed evaluation does not confirm a stable held-out-test advantage under the same adaptation budget.These findings suggest that conclusions drawn from a single curated benchmark may overstate model robustness,and that for wood defect detection,protocol breadth and source-to-target shift should be treated as central evaluation factors rather than secondary experimental details. 展开更多
关键词 Wood surface defect detection lightweight detector comparison MobileNetV3-FPN YOLOv8 benchmark sensitivity industrial visual inspection domain shift
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Optical design of wide-field and broadband light field camera for high-precision optical surface defect detection 认领 引用
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作者 Chengchen Zhou Yukun Wang +7 位作者 Yue Ding Dacheng Wang Jiucheng Nie Jialong Li Zhixi Li Zheng Zhou Shuangshuang Zhang Xiaokun Wang 《Astronomical Techniques and Instruments》 CSCD 2026年第1期64-74,共11页
To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of ... To address the challenges of high-precision optical surface defect detection,we propose a novel design for a wide-field and broadband light field camera in this work.The proposed system can achieve a 50°field of view and operates at both visible and near-infrared wavelengths.Using the principles of light field imaging,the proposed design enables 3D reconstruction of optical surfaces,thus enabling vertical surface height measurements with enhanced accuracy.Using Zemax-based simulations,we evaluate the system’s modulation transfer function,its optical aberrations,and its tolerance to shape variations through Zernike coefficient adjustments.The results demonstrate that this camera can achieve the required spatial resolution while also maintaining high imaging quality and thus offers a promising solution for advanced optical surface defect inspection. 展开更多
关键词 Optical design Defect detection Wide-field camera Broadband light field camera
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Architectural Design of RT-DETR-L for PCB Surface Defect Detection:A Systematic Comparison of Attention Mechanisms,Backbone Replacement,and Cross-Dataset Generalization 认领 引用
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作者 Tao Huang 《Journal of Electronic Research and Application》 2026年第6期14-20,共7页
Based on RT-DETR-L,this paper systematically compares five attention mechanisms(SE,CBAM,CA,ECA,and EMA)at the P3/P4/P5 outputs of the feature-pyramid neck under identical training conditions,and evaluates FasterNet ba... Based on RT-DETR-L,this paper systematically compares five attention mechanisms(SE,CBAM,CA,ECA,and EMA)at the P3/P4/P5 outputs of the feature-pyramid neck under identical training conditions,and evaluates FasterNet backbone replacement and a P2 small-object detection head as complementary improvements.Experiments reveal a performance gap of up to 4.29 percentage points(CA:93.04%to EMA:97.33%in mAP50),indicating that the choice of attention mechanism has a substantial impact on RT-DETR-type PCB detectors.EMA achieves the best mAP50(97.33%)and the highest mAP50:95(56.45%);ECA offers a competitive trade-off without increasing GFLOPs(96.69%);CA performs worst(93.04%),a 3.34 pp drop below the baseline,tentatively attributed to an architectural conflict with the AIFI encoder.FasterNet backbone replacement trades accuracy for efficiency(31%fewer parameters,40%lower GFLOPs);and,when trained from scratch on the second dataset DeepPCB,the EMA variant again yields the largest gain(mAP5089.33%,+4.83 pp over the baseline),showing that the improvement is not specific to a single dataset. 展开更多
关键词 PCB defect detection RT-DETR Attention mechanism comparison EMA FasterNet Cross-dataset generalization
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An Improved YOLOv8-Based Algorithm for Industrial Metal Surface Defect Detection 认领 引用
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作者 Yinghao Zhu Shijie Jia 《Journal of Electronic Research and Application》 2026年第4期50-68,共19页
To address challenges in industrial metal surface defect detection,including tiny defects,significant scale variation,and complex backgrounds,this study proposes an enhanced YOLOv8s-based model,termed MEAF-YOLOv8s.Bas... To address challenges in industrial metal surface defect detection,including tiny defects,significant scale variation,and complex backgrounds,this study proposes an enhanced YOLOv8s-based model,termed MEAF-YOLOv8s.Based on the original YOLOv8s architecture,the model introduces several improvements to enhance feature extraction and multi-scale representation.First,a CSP-MSEE module is incorporated in the feature extraction stage to strengthen the capture of edge and detail information of tiny defects,thereby effectively alleviating the problem of insufficient feature representation for small targets.Second,an AFRBN module is introduced to establish long-range spatial dependencies.By leveraging global contextual information,the module suppresses texture background interference,while a re-parameterization strategy is adopted to maintain the lightweight nature of the model and ensure that inference efficiency is not compromised.In addition,a CA-HFPN feature fusion structure is employed,which incorporates a direction-aware coordinate attention mechanism and a hierarchical pyramid architecture to promote precise cross-scale feature alignment and adaptive fusion,thereby improving the model’s adaptability and localization accuracy for defects of different sizes.To evaluate the proposed method,experiments are carried out on seven common defect types collected from real industrial environments.The results indicate that MEAF-YOLOv8s improves mAP50 by 4.72%and mAP50-95 by 1.28%,while decreasing the number of parameters by approximately 5M.These findings confirm that the proposed model can effectively enhance defect detection performance under complex background conditions. 展开更多
关键词 Deep learning Defect detection YOLOv8 Feature extraction Multi-scale feature fusion
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Research on Automatic Bridge Quality Defect Detection Technology Based on Image Recognition 认领 引用
18
作者 XIE Wei 《外文科技期刊数据库（文摘版）工程技术》 2026年第4期155-159,共5页
With the continuous advancement and long-term utilization of domestic transportation infrastructure, health monitoring of bridge structures and inspection of quality defects have become critical measures for ensuring ... With the continuous advancement and long-term utilization of domestic transportation infrastructure, health monitoring of bridge structures and inspection of quality defects have become critical measures for ensuring public safety and property protection. Traditional manual inspection methods for bridge defects suffer from low efficiency, reliance on personal experience, high operational risks, and challenges in unified data management. The rapid progress in computer vision and image recognition technologies has opened new possibilities for automated and intelligent bridge inspection. This paper explores automated detection methods for bridge quality defects using image recognition techniques, aiming to enhance the fairness, efficiency, and accuracy of inspection processes. First, it summarizes common types of bridge surface defects and their visual characteristics, while discussing limitations of conventional inspection approaches and the importance of image recognition technology. Subsequently, it reviews practical applications of digital image processing and computer vision in construction inspection, highlighting current shortcomings of automated systems regarding complex backgrounds, fine defects, and model adaptability. Based on these findings, the study proposes a comprehensive automated detection framework for bridge surface defect images. This solution systematically outlines the entire process from image acquisition to post-processing, featuring a specialized defect identification model developed with enhanced convolutional neural networks that improves detection capabilities for subtle cracks, surface delamination, and corrosion through structural optimization, while providing graphical visualization and quantitative data representation of inspection results. To verify the reliability of this method, researchers specifically compiled a database of bridge images containing various common defects for testing. 展开更多
关键词 Bridge inspection Image recognition Automatic defect detection Convolutional neural network Computer vision
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Transmission Line Defect Detection Algorithm Based on Improved RT-DETR Model 认领 引用
19
作者 Qi Wu 《Journal of Electronic Research and Application》 2026年第1期227-238,共12页
This paper addresses the urgent need for high-precision and high-efficiency visual perception technologies in power equipment operation and maintenance under the background of rapid development of smart grids.It point... This paper addresses the urgent need for high-precision and high-efficiency visual perception technologies in power equipment operation and maintenance under the background of rapid development of smart grids.It points out the performance limitations of the existing real-time target detection framework RT-DETR when handling small targets,dense targets,and complex backgrounds in power inspection scenarios.To overcome this bottleneck,this study proposes an improved backbone network model,DETR-EVA,based on an efficient visual attention mechanism(EVA).This model innovatively designs an attention computation structure with linear complexity by deeply integrating the EVA mechanism with the C2f module in the RT-DETR backbone network,and combines local detail perception and global dependency modeling capabilities.Its core lies in the introduction of a gated fusion mechanism,which significantly enhances the model’s ability to model long-distance contextual relationships and the adaptive adjustment efficiency of feature weights while retaining the advantages of multi-branch feature extraction and fusion of the C2f module.Experiments were conducted on an inspection image dataset containing typical power equipment targets.The results show that compared with the original RT-DETR model,DETR-EVA improves the overall accuracy index mAP50-95 by 2.5%,reduces computational complexity by 14%,and reduces the number of model parameters by 27%.This effectively verifies that the proposed method can significantly improve the detection accuracy of small targets and complex scenes while maintaining real-time detection speed,providing a better visual solution for intelligent operation and maintenance of power equipment. 展开更多
关键词 RT-DETR Defect detection Efficient vision attention C2f Small object detection
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YOLO11-SPMB:Application of an Improved Instance Segmentation Network for Tire Defect Detection 认领 引用
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作者 Haixu You Jun Xing 《Journal of Electronic Research and Application》 2026年第6期67-75,共9页
Tire quality is vital for vehicle safety,but finding surface defects automatically is hard.Current systems often fail to spot tiny flaws hidden inside complex tread patterns.Also,because defects vary in size and blend... Tire quality is vital for vehicle safety,but finding surface defects automatically is hard.Current systems often fail to spot tiny flaws hidden inside complex tread patterns.Also,because defects vary in size and blend into the dark rubber,standard models usually output broken or messy segmentation masks.To solve this,we built YOLO11-SPMB,a new instance segmentation network based on YOLO11.We designed it specifically for tire inspection.First,we added a Multi-Scale Adaptive Feature Fusion(MS-AFF)module to the network.It filters out the repeating noise from normal treads so tiny defects can stand out.Second,we created a Boundary Enhancement and Imbalance Calibration(BEIC)strategy.It uses a boundary Dice loss to keep the mask edges smooth and whole.At the same time,it adjusts class weights to help the model catch rare but major damage,fixing the data imbalance problem.Tests on our custom dataset show the model works very well.YOLO11-SPMB achieves a mAP@0.5:0.95 of 0.702,a mIoU of 0.782,and a recall of 0.928.It also runs at 60 frames per second(FPS),making it fast and accurate enough for real factory production lines. 展开更多
关键词 Tire defect detection Instance segmentation YOLOv11
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