改进YOLOv10的架空输电线路多缺陷检测方法
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上海电力大学计算机科学与技术学院 上海 201306

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TP391.4;TN911.73

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上海市科技计划项目 (23010501500) 资助


Detection method of overhead transmission line defects based on improved YOLOv10
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School of Computer Science and Technology, Shanghai University of Electric Power,Shanghai 201306, China

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    摘要:

    针对输电线路缺陷检测任务中目标尺度多样、背景复杂、目标遮挡,以及现有目标检测算法难以在实时检测的前提下保证检测精度而出现漏检、误检的问题,提出改进YOLOv10的输电线路无人机巡检缺陷检测算法TLDDet。首先设计融合部分卷积和上下文锚点注意力的高效特征融合模块(FC2FA),在降低模型参数量的同时提升模型的特征集成能力。然后使用基于多头自注意力机制的尺度内特征交互模块AIFI增强对小目标的检测效果,该模块通过加强特征图中高级语义信息的表达从而提高模型检测的准确率。最后设计遮挡感知注意网络检测头SEAM-Head,减少由于遮挡问题导致的特征丢失的问题。实验结果表明,所提出的TLDDet较原始模型YOLOv10s参数量减少33%,计算量减少30%,对输电线路多种缺陷的Precision、Recall和mAP50分别提高4.3%、2.4%和3.7%,检测速度达到143 FPS,且与其他实时检测算法的对比中具有更好的检测性能。

    Abstract:

    In response to the issues of missed and false detections in transmission line defect detection tasks caused by varying target scales, complex backgrounds, and occlusion, where existing object detection algorithms struggle to maintain detection accuracy while ensuring real-time performance, an improved YOLOv10-based UAV transmission line defect detection algorithm, TLDDet, is proposed. First, a faster C2F with attention module(FC2FA) incorporating context anchor attention is designed to enhance feature integration capabilities while reducing the model′s parameters. Then, an attention-based intrascale feature interaction module (AIFI) based on multi-head self-attention is used to improve the model′s detection performance for small objects by enhancing the representation of high-level semantic information in the feature map, thereby increasing the detection accuracy. Finally, an occlusion-aware attention detection head (SEAM-Head) is designed to reduce feature loss caused by occlusion. Experimental results show that the proposed TLDDet reduces the parameters by 33% and the computational cost by 30% compared to the original YOLOv10s model, while improving precision, recall, and mean average precisionfor various transmission line defects by 4.3%, 2.4%, and 3.7%, respectively. The detection speed reaches 143 FPS, and comparative experiments with other real-time detection algorithms demonstrate superior model performance, making TLDDet more suitable for transmission line defect detection tasks.

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李坤祥,刘大明.改进YOLOv10的架空输电线路多缺陷检测方法[J].电子测量技术,2025,48(9):156-167

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  • 在线发布日期: 2025-05-23
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