基于双目特征融合和改进FCOS检测头的输电线路缺陷识别方法

Ming Mao, Lu Liu, Wenxiang Chen, Weiqi Xiong, Xuelei Xi, Guoqiang Zhu, Yan Zhang, Shuang Wang, Yu Chen
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引用次数: 0

摘要

无人机广泛应用于输电线路巡检。检查员操作无人机拍摄传输线的图像,并分析和识别这些图像。传输线缺陷检测是无人机检测图像中的一项重要任务。提出了一种基于双目特征融合和改进FCOS检测头的传输线缺陷检测方法。首先,设计了双目特征融合模块;二是在网络中增加特征筛选模块。最后,将IoU预测分支添加到FCOS检测头。实验结果表明,本文提出的传输线缺陷检测方法能够有效识别断链和异物两种缺陷,mAP达到90.85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Transmission Line Defect Recognition Method Based on Binocular Feature Fusion and Improved FCOS Detection Head
UAVs are widely used in transmission line inspection. Inspectors operate UAVs to take images of transmission lines and analyze and identify these images. Detecting transmission line defects in UAV inspection images is an important task. This paper proposes a transmission line defect detection method based on binocular feature fusion and an improved FCOS detection head. First, a binocular feature fusion module is designed. Second, a feature screening module is added to the network. Finally, add the IoU prediction branch to the FCOS detection head. The experimental results show that the transmission line defect detection method proposed in this paper can effectively identify the two defects of broken strands and foreign objects, and the mAP reaches 90.85%.
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