Bird’s Nest Detection Algorithm for Transmission Lines Based on Deep Learning
Bird’s Nest Detection Algorithm for Transmission Lines Based on Deep Learning
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基于深度学习的输电线路鸟巢检测算法
DOI：
10.1109/cvidliccea56201.2022.9824057
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ziru Ma
中科院分区：
文献类型：
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作者：
Zhao Ge;Hongwen Li;Rui Yang;Hai;Shaotong Pei;Zhihui Jia;Ziru Ma
输电塔上的鸟巢可能会导致鸟闪事故的发生，威胁电网的安全可靠运行。实现燕窝的自主识别和定位一直是研究热点。随着无人机巡检应用的逐步深入，对燕窝识别算法的精度和速度提出了更高的要求。提出了一种基于YOLOv5的燕窝缺陷识别方法，该方法由骨干网络、FPN和YOLO头组成。经过多轮对输电线路鸟巢缺陷数据库和模型的构建训练，实现了鸟巢的自主识别和定位。结果表明，YOLOv5模型对燕窝的识别率可达83.4%，FPS可达85.32。本文提出的基于YOLOv5的识别算法能够满足无人机巡检对目标检测的实时性和准确性要求。
The bird’s nest on the power transmission tower may cause the occurrence of bird flash accidents and threaten the safe and reliable operation of the power grid. The realization of the independent identification and positioning of the bird’s nest has always been a research hotspot. With the gradual deepening of the application of UAV inspection, higher requirements are put forward for the accuracy and speed of the bird’s nest recognition algorithm. This paper proposes a bird’s nest defect recognition method based on YOLOv5, which is composed of backbone network, FPN and YOLO head. After multiple rounds of training on the construction of the bird’s nest defect database and model of the transmission line, the independent identification and positioning of the bird’s nest has been realized. The results show that the recognition rate of the YOLOv5 model for the bird’s nest can reach 83.4%, and the FPS can reach 85.32. The recognition algorithm based on YOLOv5 proposed in this paper can meet the real-time and accuracy requirements of UAV inspection for target detection.