MCI-GLA Plug-In Suitable for YOLO Series Models for Transmission Line Insulator Defect Detection

Yaru Wang, Xiaoke Song, Lilong Feng, Yongjie Zhai, Zhenbing Zhao, Shiyin Zhang, Qianming Wang

Abstract: In the detection of insulator defects on transmission lines, the detection precision is still not ideal, primarily attributed to the significant variation in target scale and complex image backgrounds. We propose the multiscale channel information (MCI)-global-local attention (GLA), a plug-in designed for YOLO series models, featuring two modules: the MCI extraction module and the GLA based on context information module (GLA-CI). MCI comprehensively extracts and utilizes multiscale feature map information, while GLA-CI captures both global context information and local spatial details, thereby augmenting the learning capability of networks. Experimental results indicate that the MCI-GLA plug-in improves the average precision (AP) of YOLOv4 to YOLOv8 models in detecting insulator breakage defects by 7.3%, 4.6%, 4.5%, 4.0%, and 5.3%, respectively. In particular, YOLOv7+MCI-GLA exhibits superior precision and inference time compared to other methods on self-constructed and public datasets. The code for this article can be found at https://github.com/falian0527/MCI-GLA.

External IDs:dblp:journals/tim/WangSFZZZW24