Insulator Defect Detection Model in Rainy Scene Based on RID-YOLOv7
QI Haoyu
TAN Aiguo
LIANG Huijun
ZHONG Jianwei
YANG Yongchao
CHEN Wentao
Abstract：Aiming at the problems of poor recognition effect and slow inference speed of existing transmission line insulator defect detection models for insulators in rainy complex scenes,a rain insulator detection-you only look once version 7(RID-YOLOv7)model which was a lightweight insulator defect detection model in rainy scenes was proposed on the basis of YOLOv7-tiny.Firstly,the optimal embedding position of the coordinate attention(CA)mechanism in the backbone feature extraction network was explored to improve the model's ability to extract key features of the target position.Secondly,ghost shuffle convolution(GSConv)and vortex of vectorized ghost shuffle cross stage partial(VoV-GSCSP)were introduced into the neck feature fusion network to greatly reduce the inference time.Finally,wise intersection over union(WIoU)was used to optimize the bounding box positioning loss function and improve the convergence efficiency of the model.The results showed that compared with the original YOLOv7-tiny,the precision,recall and mean average precision of the RID-YOLOv7 model were improved by 2.41%,5.44%and 3.22%,respectively.The inference speed reached 88.7 frames/s,which effectively balanced the detection speed and accuracy.The model is more suitable for real-time detection of transmission line insulator defects in rainy scenes.
Keywords：insulator defect detectionrainy scenelightweightcoordinate attention mechanismloss functiondeep learning
Publication Date：2024-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 233-240 )