计算机工程与应用2024，Vol.60Issue(4)：289-297,9.DOI:10.3778/j.issn.1002-8331.2302-0165
改进YOLOv5混合样本训练的绝缘子伞盘脱落缺陷检测方法
Improved YOLOv5 Mixed Sample Training for Detection of Insulator Umbrella Plate Falling Defects
李洵 1甘润东 1钱俊凤 1张世恒 2赵文彬 2王道累2
作者信息
- 1. 贵州电网有限责任公司 信息中心,贵阳 550003
- 2. 上海电力大学 能源与机械工程学院,上海 200240
- 折叠
摘要
Abstract
In order to realize the accurate location and identification of insulator string and umbrella plate falling defects during transmission line inspection,this paper proposes an insulator defect detection model based on improved YOLOv5 mixed sample training.Firstly,aiming at the scarcity of insulator defect images,a hybrid sample data generation method is proposed,which combines GrabCut algorithm with image fusion technology to expand the data set.Then,according to the shape characteristics of insulators and defects,the long edge definition method and CSL(circular smooth label)are used to redefine the coordinate parameters of the model feature extraction area.By adding angle information,more accu-rate feature extraction is realized.Finally,the CSPDarkNet backbone network is optimized by fusing some feature layers in the Backbone with the features extracted by PAN(path aggregation network).The improved YOLOv5 CSPDarkNet model increases the detection accuracy of insulator defects by 2.8 percentage points compared with the improved model,and the detection rate is 20.5 FPS.The experimental results show that the improved insulator defect identification method basically meets the needs of practical application.关键词
特征融合/YOLOv5/旋转框/伞盘脱落缺陷Key words
feature fusion/YOLOv5/rotating frame/umbrella plate falling defect分类
信息技术与安全科学引用本文复制引用
李洵,甘润东,钱俊凤,张世恒,赵文彬,王道累..改进YOLOv5混合样本训练的绝缘子伞盘脱落缺陷检测方法[J].计算机工程与应用,2024,60(4):289-297,9.
