周亚罗,王海明,刘文广,张瑞成

• 1:华北理工大学电气工程学院
• 2:河北省风光氢储安全监测与智能运行技术创新中心
• 3:首钢京唐钢铁联合有限责任公司

摘要(Abstract)：

针对自动驾驶交通场景中车辆检测目标尺度分布不均、背景干扰显著、小目标结构失真以及计算复杂度高等问题，提出一种基于YOLOv11n的改进算法ELD-YOLO算法。通过引入C3k2-EMSC模块，增强多尺度特征提取能力，在复杂交通场景下获得更丰富的上下文信息；LSDECD模块利用共享卷积与群归一化强化特征表达，减少冗余响应；通过动态采样（DySample）提升小目标与边缘特征表征能力。试验结果表明：在KITTI与BDD100K数据集上，ELD-YOLO参数量较YOLOv11n降低16%,mAP@0.5分别达到89.7%和52.7%，降低模型复杂度的同时，显著提升检测精度与鲁棒性。

关键词(KeyWords)：

Abstract:

Keywords:

基金项目(Foundation): 河北省自然科学基金项目（F2018209201）;; 唐山市科技局科技计划项目（22130213G）

作者(Author): 周亚罗,王海明,刘文广,张瑞成

DOI: 10.19620/j.cnki.1000-3703.20250865

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