With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
AI技术为突破传统的优化与控制理论、方法,推动智能优化与控制提供了重要途径和基础支撑。为探析AI驱动智能优化与控制的研究脉络、热点和趋势,对近10年中国知网(CNKI)和Web of Science中收录的AI驱动智能优化与控制相关文献进行筛选,...AI技术为突破传统的优化与控制理论、方法,推动智能优化与控制提供了重要途径和基础支撑。为探析AI驱动智能优化与控制的研究脉络、热点和趋势,对近10年中国知网(CNKI)和Web of Science中收录的AI驱动智能优化与控制相关文献进行筛选,得到中文文献数据共5814条,英文文献数据共5208条;使用CiteSpaee6.1.R6软件以及VOSviewer1.8.18软件进行可视化分析,并绘制关键词共现图谱、时间线演化图谱、作者聚类图谱等知识图谱,进行文献计量分析。结果表明:有关AI驱动智能优化与控制的文献数量整体处于上升趋势,在发文国家中以中国和美国文献数量最多。国际上对该方向的研究主要是围绕系统进行的,并尝试通过机器学习解决系统难题;国内研究热点聚集在深度学习的方向。根据研究现状和热点,对该领域进行了发展趋势预测,为AI驱动智能优化与控制领域研究提供了参考。展开更多
摘要With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
摘要AI技术为突破传统的优化与控制理论、方法,推动智能优化与控制提供了重要途径和基础支撑。为探析AI驱动智能优化与控制的研究脉络、热点和趋势,对近10年中国知网(CNKI)和Web of Science中收录的AI驱动智能优化与控制相关文献进行筛选,得到中文文献数据共5814条,英文文献数据共5208条;使用CiteSpaee6.1.R6软件以及VOSviewer1.8.18软件进行可视化分析,并绘制关键词共现图谱、时间线演化图谱、作者聚类图谱等知识图谱,进行文献计量分析。结果表明:有关AI驱动智能优化与控制的文献数量整体处于上升趋势,在发文国家中以中国和美国文献数量最多。国际上对该方向的研究主要是围绕系统进行的,并尝试通过机器学习解决系统难题;国内研究热点聚集在深度学习的方向。根据研究现状和热点,对该领域进行了发展趋势预测,为AI驱动智能优化与控制领域研究提供了参考。