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Deformable Convolution-Enhanced Adaptive Contrastive Learning for Robust H&E to CK19 Virtual Staining in Thyroid Pathology 认领 引用
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作者 Wenxu Gong 《Science International Innovative Medicine》 2026年第1期1-9,共9页
To address the subjective variability of H&E staining in differentiating follicular variant papillary thyroid carcinoma(FVPTC)from benign lesions,this study proposes an enhanced virtual staining framework designed... To address the subjective variability of H&E staining in differentiating follicular variant papillary thyroid carcinoma(FVPTC)from benign lesions,this study proposes an enhanced virtual staining framework designed to achieve high-fidelity digital transformation from H&E to CK19 immunohistochemical(IHC)staining.Addressing the limitations of traditional supervised learning models—which often produce blurring or artifacts when faced with"misalignment"caused by tissue deformation—this framework innovatively integrates Deformable Convolution v4(DCNv4)into a ResNet-based generator.By learning dynamic spatial sampling offsets,DCNv4 enables the network to perform"active alignment"against non-linear geometric distortions in pathological sections.The objective function incorporates Adaptive Supervised Contrastive Loss(ASP Loss),Multi-scale Gaussian Pyramid Loss(GP Loss),and PatchNCE loss,allowing the model to automatically downweight misaligned regions and ensure consistency in both macro-structure and micro-features without requiring strict pixel-level alignment.Experimental results on a thyroid pathology dataset demonstrate that this approach significantly reduces the Fréchet Inception Distance(FID)to 130 while maintaining a high Structural Similarity Index(SSIM)of 0.43.Furthermore,by optimizing the discriminator configuration,the total framework parameters were reduced from 11.26M to 8.29M.This provides a robust,lightweight,and high-performance virtual staining solution to support clinical pathological diagnosis. 展开更多
关键词 Virtual Staining Thyroid Pathology Deformable Convolution Contrastive Learning Image-to-Image Translation
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A Remote Sensing Image Semantic Segmentation Method by Combining Deformable Convolution with Conditional Random Fields 认领 引用 被引量：13
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作者 Zongcheng ZUO Wen ZHANG Dongying ZHANG 《Journal of Geodesy and Geoinformation Science》 2020年第3期39-49,共11页
Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the a... Currently,deep convolutional neural networks have made great progress in the field of semantic segmentation.Because of the fixed convolution kernel geometry,standard convolution neural networks have been limited the ability to simulate geometric transformations.Therefore,a deformable convolution is introduced to enhance the adaptability of convolutional networks to spatial transformation.Considering that the deep convolutional neural networks cannot adequately segment the local objects at the output layer due to using the pooling layers in neural network architecture.To overcome this shortcoming,the rough prediction segmentation results of the neural network output layer will be processed by fully connected conditional random fields to improve the ability of image segmentation.The proposed method can easily be trained by end-to-end using standard backpropagation algorithms.Finally,the proposed method is tested on the ISPRS dataset.The results show that the proposed method can effectively overcome the influence of the complex structure of the segmentation object and obtain state-of-the-art accuracy on the ISPRS Vaihingen 2D semantic labeling dataset. 展开更多
关键词 high-resolution remote sensing image semantic segmentation deformable convolution network conditions random fields
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Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection 认领 引用 被引量：1
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作者 Jielin Jiang Chao Cui +1 位作者 Xiaolong Xu Yan Cui 《Intelligent Automation & Soft Computing》 2024年第4期725-744,共20页
In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.... In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively poor.Deep learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex shapes.Therefore,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect detection.In MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects.On this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects.In this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds.The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time. 展开更多
关键词 Fabric defect detection multi-layer features deformable convolution
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CW-HRNet:Constrained Deformable Sampling and Wavelet-Guided Enhancement for Lightweight Crack Segmentation 认领 引用
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作者 Dewang Ma 《Journal of Electronic Research and Application》 2025年第5期269-280,共12页
This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two ke... This paper presents CW-HRNet,a high-resolution,lightweight crack segmentation network designed to address challenges in complex scenes with slender,deformable,and blurred crack structures.The model incorporates two key modules:Constrained Deformable Convolution(CDC),which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets,and the Wavelet Frequency Enhancement Module(WFEM),which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures.Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance,achieving 82.39%mIoU with only 7.49M parameters and 10.34 GFLOPs,outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead.The model also shows strong cross-dataset generalization,achieving 60.01%mIoU and 66.22%F1 on Asphalt3k without fine-tuning.These results highlight CW-HRNet’s favorable accuracyefficiency trade-off for real-world crack segmentation tasks. 展开更多
关键词 Crack segmentation Lightweight semantic segmentation Deformable convolution Wavelet transform Road infrastructure
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Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems 认领 引用
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作者 Syed Sajid Ullah Muhammad Zunair Zamir +1 位作者 Ahsan Ishfaq Salman Khan 《Journal on Artificial Intelligence》 2025年第1期255-274,共20页
Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional B... Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional Block Attention Module(CBAM),and Deformable Convolutional Networks v2(DCNv2).The Ghost Module streamlines feature generation to reduce redundancy,CBAM applies channel and spatial attention to improve feature focus,and DCNv2 enables adaptability to geometric variations in vehicle shapes.These components work together to improve both accuracy and computational efficiency.Evaluated on the KITTI dataset,the proposed model achieves 95.4%mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision,93.7% recall,and a 94.93%F1-score.Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics.An ablation study is also conducted to quantify the individual and combined contributions of GhostModule,CBAM,and DCNv2,highlighting their effectiveness in improving detection performance.By addressing feature redundancy,attention refinement,and spatial adaptability,the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios. 展开更多
关键词 YOLOv8n vehicle detection deformable convolutional networks(DCNv2) ghost module convolutional block attention module(CBAM) attention mechanisms
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A pedestrian detection model based on YOLO for dense scenes 认领 引用
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作者 Shujian XING Furong WANG Hong WANG 《Optoelectronics Letters》 EI 2026年第4期229-235,共7页
To address challenges in pedestrian detection within dense scenes,including high crowd density,severe occlusion,and overlapping individuals,an improved you only look once(YOLO)-based algorithm is proposed.First,deform... To address challenges in pedestrian detection within dense scenes,including high crowd density,severe occlusion,and overlapping individuals,an improved you only look once(YOLO)-based algorithm is proposed.First,deformable convolutions are employed to replace standard convolutions,enhancing the model’s adaptability to variations in shape and appearance under occlusions.Second,a multi-dimensional attention module is designed to emphasize critical local regions and extract more precise feature information.Lastly,a diagonal difference intersection-over-union(IoU)loss function is introduced,which incorporates a measure of the Euclidean distance difference between the main diagonal points of predicted and ground truth bounding boxes,thereby enhancing detection accuracy and regression performance.Experimental results demonstrate that the enhanced algorithm achieves a mean average precision at IoU=0.5(mAP50)of 75.1%on the public dense pedestrian dataset WiderPerson,an improvement of 1.8%over the original YOLOv5 model,showcasing superior detection performance. 展开更多
关键词 multi dimensional attention standard convolutionsenhancing pedestrian detection YOLO deformable convolutions emphasize critical local regions diagonal difference IoU loss dense scenesincluding
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A transfer learning framework for the real-time detection of atmospheric gravity waves from All-Sky Airglow Imager 认领 引用 被引量：1
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作者 YuBin He QingChen Xu +6 位作者 YaJun Zhu QinZeng Li Cui Tu Bing Cai Wei Yuan XinYing Wang Feng Wei 《Earth and Planetary Physics》 EI CSCD 2026年第3期454-462,共9页
Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual s... Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual screening methods and existing machine learning approaches do not meet the demands of practical station monitoring,which has significantly impeded climatological statistical research based on AGWs.Therefore,a real-time detection framework for ground-based airglow gravity waves that integrates transfer learning with adaptive image preprocessing has been proposed.By employing wavelength-adaptive median filtering and multiscale fusion,the framework effectively suppresses stellar noise while preserving weak gravity wave features.The model utilizes an EfficientNet-B3(convolutional neural network)backbone enhanced with a deformable convolutional layer,trained via a two-stage strategy:A frozen phase prevents overfitting by locking the lower level feature extractor,and a fine-tuning phase optimizes the deformable convolution through cosine annealing and layered optimization.This approach improves both feature transfer efficiency and gravity wave detection sensitivity.The resulting lightweight model achieves 91.2%accuracy with millisecond-level inference speed(23 ms per frame). 展开更多
关键词 atmospheric gravity waves All-Sky Airglow Imager real-time detection multiscale fusion transfer learning EfficientNet-B3 deformable convolution
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ColorAlignNet:a Reference-Based Video Colorization Network with Temporal Aggregation 认领 引用
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作者 ZHU Wenzhi WANG Tong 《Journal of Donghua University(English Edition)》 CAS 2026年第2期94-102,共9页
Video colorization is an important technique to breathe life back into old movies.While current colorization methods work well on still images and low-motion video data,they often struggle with complex dynamic scenes.... Video colorization is an important technique to breathe life back into old movies.While current colorization methods work well on still images and low-motion video data,they often struggle with complex dynamic scenes.To address this problem,this study proposes ColorAlignNet,a reference-based video colorization network with temporal aggregation.The network uses source-reference attention to propagate color information from reference frames to grayscale frames,guaranteeing color accuracy,and uses deformable convolution to align features of adjacent frames to enhance temporal consistency.Finally,we use the cyclic transformer module to reconstruct the final prediction results.Extensive experimental results demonstrate that ColorAlignNet achieves excellent performance on the DAVIS and Videvo datasets,outperforming other state-of-the-art methods on both the learned perceptual image patch similarity(LPIPS)and color distribution consistency(CDC)metrics. 展开更多
关键词 deformable convolution video colorization Swintransformer
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Parallel feature fusion framework for image dehazing driven by hazedetail collaboration 认领 引用
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作者 YU Xuan MA Jiaqing YU Leiyi 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2026年第2期232-242,共11页
Image dehazing remains a highly ill-posed problem in low-level computer vision,primarily due to the complex coupling of spatially variant haze and high-frequency background details.Existing deep learning-based methods... Image dehazing remains a highly ill-posed problem in low-level computer vision,primarily due to the complex coupling of spatially variant haze and high-frequency background details.Existing deep learning-based methods often struggle to balance the trade-off between aggressive haze removal and the preservation of fine textures,frequently resulting in color distortion,halos,or detail loss.To address these limitations and achieve high-fidelity restoration,this paper proposes a parallel feature fusion framework driven by a hazedetail collaboration mechanism.Specifically,the proposed framework adopts a dual-branch parallel architecture to disentangle the dehazing process.The upper branch functions as a haze layer extraction network.It employs a Res2Net-based encoder to capture multi-scale semantic features and integrates a novel deformable convolution-residual hybrid attention module.By dynamically adjusting the receptive fields,this module precisely characterizes non-uniform haze distributions and models long-range dependencies.Simultaneously,the lower branch serves as a detail compensation network,leveraging context detail information blocks with multi-scale dilated convolutions to aggregate contextual cues and reinforce the representation of high-frequency textural details.Subsequently,a fusion network performs adaptive feature integration,effectively merging the extracted haze features with the enhanced detail information to reconstruct the hazefree image.To ensure robust training,a dual-supervision mechanism is introduced,combining a feature regularization loss to align feature distributions in the latent space and a reconstruction loss to constrain pixel-level content fidelity.Extensive quantitative and qualitative experiments are conducted on both synthetic benchmarks and real-world datasets.The results demonstrated that the proposed algorithm delivered superior performance,achieving higher peak signal-to-noise ratio and structural similarity scores compared to state-of-the-art methods.Visual comparisons further confirmed that our method effectively removed dense haze while recovering vivid colors and sharp structural details without introducing artifacts. 展开更多
关键词 image dehazing Res2Net attention mechanism deformable convolution texture preservation feature supervision
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An Enhanced Feature Neural Network and Its Application in Detection of Colorectal Polyps 认领 引用
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作者 LI Hailong LIU Guohua ZHAO Meng 《Journal of Donghua University(English Edition)》 CAS 2026年第1期32-40,共9页
The colorectal cancer is one of the most common and lethal cancers,and colorectal polyps,as precancerous lesions,can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes,thereby promoting ... The colorectal cancer is one of the most common and lethal cancers,and colorectal polyps,as precancerous lesions,can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes,thereby promoting the irreversible progression of colorectal cancer.We propose a YOLO based model and name it EF-YOLO.It incorporates transformer to extract contextual information about the colorectal polyps.Simultaneously,leveraging the morphological characteristics of colorectal polyps,we design a brand-new module,namely advanced multi-scale aggregation(AMSA),to replace the traditional multi-scale module.The backbone adopts deformable convolutional network-maxpool(DCN-MP)to enhance feature extraction while adaptively sampling points to better match the shapes of colorectal polyps.By combining coordinate attention(CA),this model maximizes the use of positional and channel information,more effectively extracting features of colorectal polyps,directing the model’s attention toward the colorectal polyp region.EF-YOLO has made advancement on the merged Kvasir-SEG and CVC-ClinicDB dataset.Compared to the original model,the mean average precision(mAP)of EF-YOLO increases and reaches 96.60%,meeting automated colorectal polyp detection requirements. 展开更多
关键词 colorectal polyp YOLO transformer deformable convolutional network-maxpool(DCN-MP) coordinate attention(CA)
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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences 认领 引用 被引量：1
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作者 Yicheng ZHAO Han ZHANG +3 位作者 Ping LU Ping LI Enhua WU Bin SHENG 《虚拟现实与智能硬件（中英文）》 EI 2022年第5期432-443,共12页
Background Exploring correspondences across multiview images is the basis of various computer vision tasks.However,most existing methods have limited accuracy under challenging conditions.Method To learn more robust a... Background Exploring correspondences across multiview images is the basis of various computer vision tasks.However,most existing methods have limited accuracy under challenging conditions.Method To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this study.First,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive fields.The dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust correspondence.Second,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate correspondences.Result Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization benchmark.Specifically,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset. 展开更多
关键词 Image matching Deformable convolution network Sparse-to-dense matching
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A Deformable Network with Attention Mechanism for Retinal Vessel Segmentation 认领 引用
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作者 Xiaolong Zhu Wenjian Li +2 位作者 Weihang Zhang Dongwei Li Huiqi Li 《Journal of Beijing Institute of Technology》 EI CAS 2024年第3期186-193,共8页
The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segm... The intensive application of deep learning in medical image processing has facilitated the advancement of automatic retinal vessel segmentation research.To overcome the limitation that traditional U-shaped vessel segmentation networks fail to extract features in fundus image sufficiently,we propose a novel network(DSeU-net)based on deformable convolution and squeeze excitation residual module.The deformable convolution is utilized to dynamically adjust the receptive field for the feature extraction of retinal vessel.And the squeeze excitation residual module is used to scale the weights of the low-level features so that the network learns the complex relationships of the different feature layers efficiently.We validate the DSeU-net on three public retinal vessel segmentation datasets including DRIVE,CHASEDB1,and STARE,and the experimental results demonstrate the satisfactory segmentation performance of the network. 展开更多
关键词 retinal vessel segmentation deformable convolution attention mechanism deep learning
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Deep Learning-Based Algorithm for Robust Object Detection in Flooded and Rainy Environments 认领 引用
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作者 Pengfei Wang Jiwu Sun +4 位作者 Lu Lu Hongchen Li Hongzhe Liu Cheng Xu Yongqiang Liu 《Computers, Materials & Continua》 SCIE EI 2025年第8期2883-2903,共21页
Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms.Traditional methods often struggle to address issues such as image blurring,dynamic noise interfe... Flooding and heavy rainfall under extreme weather conditions pose significant challenges to target detection algorithms.Traditional methods often struggle to address issues such as image blurring,dynamic noise interference,and variations in target scale.Conventional neural network(CNN)-based target detection approaches face notable limitations in such adverse weather scenarios,primarily due to the fixed geometric sampling structures that hinder adaptability to complex backgrounds and dynamically changing object appearances.To address these challenges,this paper proposes an optimized YOLOv9 model incorporating an improved deformable convolutional network(DCN)enhanced with a multi-scale dilated attention(MSDA)mechanism.Specifically,the DCN module enhances themodel’s adaptability to target deformation and noise interference by adaptively adjusting the sampling grid positions,while also integrating feature amplitude modulation to further improve robustness.Additionally,theMSDA module is introduced to capture contextual features acrossmultiple scales,effectively addressing issues related to target occlusion and scale variation commonly encountered in flood-affected environments.Experimental evaluations are conducted on the ISE-UFDS and UA-DETRAC datasets.The results demonstrate that the proposedmodel significantly outperforms state-of-the-art methods in key evaluation metrics,including precision,recall,F1-score,and mAP(Mean Average Precision).Notably,the model exhibits superior robustness and generalization performance under simulated severe weather conditions,offering reliable technical support for disaster emergency response systems.This study contributes to enhancing the accuracy and real-time capabilities of flood early warning systems,thereby supporting more effective disaster mitigation strategies. 展开更多
关键词 YOLO vehicle detection flood deformable convolutional networks multi-scale dilated attention
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DCA-YOLO:Detection Algorithm for YOLOv8 Pulmonary Nodules Based on Attention Mechanism Optimization 认领 引用 被引量：3
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作者 SONG Yongsheng LIU Guohua 《Journal of Donghua University(English Edition)》 CAS 2025年第1期78-87,共10页
Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially... Pulmonary nodules represent an early manifestation of lung cancer.However,pulmonary nodules only constitute a small portion of the overall image,posing challenges for physicians in image interpretation and potentially leading to false positives or missed detections.To solve these problems,the YOLOv8 network is enhanced by adding deformable convolution and atrous spatial pyramid pooling(ASPP),along with the integration of a coordinate attention(CA)mechanism.This allows the network to focus on small targets while expanding the receptive field without losing resolution.At the same time,context information on the target is gathered and feature expression is enhanced by attention modules in different directions.It effectively improves the positioning accuracy and achieves good results on the LUNA16 dataset.Compared with other detection algorithms,it improves the accuracy of pulmonary nodule detection to a certain extent. 展开更多
关键词 pulmonary nodule YOLOv8 network object detection deformable convolution atrous spatial pyramid pooling(ASPP) coordinate attention(CA)mechanism
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Development and Application of a Large Vision Model for Railway Industry 认领 引用
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作者 DAI Mingrui LI Wenhao +4 位作者 SHI Weifeng LI Guohua YANG Taocun DU Wenran SHEN Meiying 《Chinese Railways》 2025年第2期3-15,共13页
Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time... Vision applications in the railway sector often face challenges such as complex and dynamic scenarios,coupled with a limited number of effective samples.Designing small models for individual scenarios is not only time-consuming and resource-intensive but also fails to meet the diverse business needs.Therefore,developing large vision models specifically for the railway industry is of critical importance.This paper examines and explores potential application scenarios for large vision models within the railway sector,proposing a solution for their development.The research builds upon the UPerNet network,utilizing InternImage to replace the original backbone network,thereby enhancing the model's ability to capture details of image targets.To further improve model robustness,Semantic-Aware Normalization(SAN)and Semantic-Aware Whitening(SAW)attention mechanisms are introduced in place of the original pyramid pooling module.Additionally,the integration of spatial attention and channel attention replaces the original decoding part,allowing for dynamic adjustments to attention across various regions.Finally,datasets for railway scenarios were established through semi-automatic annotation.The experimental results indicate that the improved UPerNet_InternImage large vision model proposed for the railway industry has potential to enhance the segmentation accuracy and robustness.The model exhibits faster convergence speeds and improved effectiveness when tackling segmentation tasks in specific railway scenarios.It offers new insights and methodologies for addressing issues prevalent in railway vision scenarios. 展开更多
关键词 artificial intelligence deformable convolution attention mechanism semantic segmentation large vision model large models for railway industry
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HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multiscale Feature Pyramid for Document Layout Analysis 认领 引用 被引量：3
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作者 Honghong Zhang Canhui Xu +3 位作者 Cao Shi Henyue Bi Yuteng Li Sami Mian 《Journal of Artificial Intelligence and Technology》 2023年第1期10-17,共8页
Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neu... Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neural networks(CNNs)are adopted as the feature extraction networks.In this paper,a hybrid spatial-channel attention network(HSCA-Net)is proposed to improve feature extraction capability by introducing attention mechanism to explore more salient properties within document pages.The HSCA-Net consists of spatial attention module(SAM),channel attention module(CAM),and designed lateral attention connection.CAM adaptively adjusts channel feature responses by emphasizing selective information,which depends on the contribution of the features of each channel.SAM guides CNNs to focus on the informative contents and capture global context information among page objects.The lateral attention connection incorporates SAM and CAM into multiscale feature pyramid network,and thus retains original feature information.The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets such as PubLayNet,ICDAR-POD,and Article Regions.Experimental results demonstrate that HSCA-Net achieves state-of-the-art performance on document layout analysis task. 展开更多
关键词 layout analysis attention mechanism deep learning deformable convolution
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Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7 认领 引用
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作者 Yongliang Yang Linghua Xu +2 位作者 Maolin Luo Xiao Wang Min Cao 《Computers, Materials & Continua》 SCIE EI 2024年第2期2741-2765,共25页
Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detec... Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios. 展开更多
关键词 University laboratory personnel behavior YOLOv7 deformable convolutional networks attention module intersection over union
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Method to Appraise Dangerous Class of Building Masonry Component Based on DC-YOLO Model 认领 引用
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作者 Hongrui Zhang Wenxue Wei +2 位作者 Xinguang Xiao Song Yang Wanlu Shao 《Computers, Materials & Continua》 SCIE EI 2020年第4期457-468,共12页
This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention,thereby making the appraisal results more objective.It is an automated metho... This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention,thereby making the appraisal results more objective.It is an automated method designed based on deep learning and target detection algorithms to appraise the dangerous class of building masonry component.Specifically,it(1)adopted K-means clustering to obtain the quantity and size of the prior boxes;(2)expanded the grid size to improve identification to small targets;(3)introduced in deformable convolution to adapt to the irregular shape of the masonry component cracks.The experimental results show that,comparing with the conventional method,the DC-YOLO model has better recognition rates for various targets to different extents,and achieves good effects in precision,recall rate and F1 value,which indicates the good performance in classifying dangerous classes of building masonry component. 展开更多
关键词 Deep learning masonry component appraisal of dangerous class deformable convolution
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DT-Net:Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation 认领 引用 被引量：2
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作者 Wenran Jia Simin Ma +1 位作者 Peng Geng Yan Sun 《Computers, Materials & Continua》 SCIE EI 2023年第9期3393-3411,共19页
Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high ... Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high noise,and complex structure.It makes vessel segmentation very challenging.Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network(U-Net)models,and they have many limitations and shortcomings,such as the loss of microvascular details at the end of the vessels.We address the limitations of convolution by introducing the transformer into retinal vessel segmentation.Therefore,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named DT-Net.Firstly,multi-scale image features are extracted by deformable convolution and multi-head selfattention(MHSA).Secondly,image information is recovered,and vessel morphology is refined by the proposed transformer decoder block.Finally,the local prediction results are obtained by the side output layer.The accuracy of the vessel segmentation is improved by the hybrid loss function.Experimental results show that our method obtains good segmentation performance on Specificity(SP),Sensitivity(SE),Accuracy(ACC),Curve(AUC),and F1-score on three publicly available fundus datasets such as DRIVE,STARE,and CHASE_DB1. 展开更多
关键词 Retinal vessel segmentation deformable convolution multi-scale transformer hybrid loss function
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Research on Facial Fatigue Detection of Drivers with Multi-feature Fusion 认领 引用 被引量：1
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作者 YE Yuxuan ZHOU Xianchun +2 位作者 WANG Wenyan YANG Chuanbin ZOU Qingyu 《Instrumentation》 2023年第1期23-31,共9页
In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face dete... In order to solve the shortcomings of current fatigue detection methods such as low accuracy or poor real-time performance,a fatigue detection method based on multi-feature fusion is proposed.Firstly,the HOG face detection algorithm and KCF target tracking algorithm are integrated and deformable convolutional neural network is introduced to identify the state of extracted eyes and mouth,fast track the detected faces and extract continuous and stable target faces for more efficient extraction.Then the head pose algorithm is introduced to detect the driver’s head in real time and obtain the driver’s head state information.Finally,a multi-feature fusion fatigue detection method is proposed based on the state of the eyes,mouth and head.According to the experimental results,the proposed method can detect the driver’s fatigue state in real time with high accuracy and good robustness compared with the current fatigue detection algorithms. 展开更多
关键词 HOG Face Posture Detection Deformable Convolution Multi-feature Fusion Fatigue Detection
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