计算机工程与应用2024，Vol.60Issue(4)：183-191,9.DOI:10.3778/j.issn.1002-8331.2306-0094
改进YOLO v7的绝缘子检测与定位
Improving Detection and Positioning of Insulators in YOLO v7
摘要
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.关键词
目标检测/深度学习/无人机巡检图像/绝缘子识别Key words
object detection/deep learning/UAV patrol image/insulator identification分类
信息技术与安全科学引用本文复制引用
张剑锐,魏霞,张林鍹,陈燕楠,卢杰..改进YOLO v7的绝缘子检测与定位[J].计算机工程与应用,2024,60(4):183-191,9.基金项目
新疆维吾尔自治区自然科学基金(2022D01C431) （2022D01C431）
新疆维吾尔自治区青年科学基金(2022D01C693). （2022D01C693）
