Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (4): 183-191.DOI: 10.3778/j.issn.1002-8331.2306-0094

• Pattern Recognition and Artificial Intelligence • Previous Articles     Next Articles

Improving Detection and Positioning of Insulators in YOLO v7

ZHANG Jianrui, WEI Xia, ZHANG Linxuan, CHEN Yannan, LU Jie   

  1. 1. School of Electrical Engineering, Xinjiang University, Urumqi 830017, China
    2. National Computer Integrated Manufacturing System Engineering Research Center, Tsinghua University, Beijing 100084, China
  • Online:2024-02-15 Published:2024-02-15

改进YOLO v7的绝缘子检测与定位


  1. 1. 新疆大学  电气工程学院,乌鲁木齐  830017
    2. 清华大学  国家计算机集成制造系统工程技术研究中心,北京  100084

Abstract: This paper aims to address the problems of low accuracy and high leakage rate due to the influence of different insulator sizes and background interference in the target detection task of power systems. Firstly, a convolutional block attention module (CBAM) is added to the YOLO v7 backbone network to make the network model pay more attention to the insulator features from both channel and space aspects and reduce the leakage rate in insulator detection. Secondly, a concentrated feature pyramid (CFP) is added to the deeper layer of the network model to allow the information exchange and aggregation of feature maps at different scales, thus obtaining more comprehensive insulator features and improving insulator detection accuracy. Finally, the k-means algorithm is used to cluster the preselected frames to obtain the most suitable insulator preselected frame size. The experimental results show that the improved YOLO v7 network model has a detection mAP (mean average precision) of 96.2%, a precision of 90.8%, and a recall of 93.8%. The improved method in this paper has a wide application prospect in the insulator detection of power systems.

Key words: object detection, deep learning, UAV patrol image, insulator identification

摘要: 针对电力系统目标检测任务中绝缘子大小不一、背景干扰等影响而导致精度低、漏检率高的问题,提出了基于改进YOLO v7绝缘子检测与定位方法。在YOLO v7骨干网络中加入轻量级注意力机制(convolutional block attention module, CBAM),使网络模型从通道、空间两个方面更加关注绝缘子特征,降低绝缘子检测中的漏检率。在网络模型深层添加集中特征金字塔(concentrated feature pyramid, CFP),使不同尺度的特征图进行信息交换和聚合,进而获得更加全面的绝缘子特征,提高绝缘子检测精度。通过k-means算法对预选框聚类,得到最适合绝缘子预选框大小。实验结果表明,改进以后的YOLO v7网络模型平均检测精度(mean average precision, mAP)达到96.2%,精准率为90.8%,召回率为93.8%。改进的方法在电力系统绝缘子检测中具有较广泛的应用前景。

关键词: 目标检测, 深度学习, 无人机巡检图像, 绝缘子识别