MCI-GLA Plug-In Suitable for YOLO Series Models for Transmission Line Insulator Defect Detection
Yaru Wang, xiumin 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 MCI-GLA, a plug-in designed for YOLO series models, featuring two modules: the multi-scale channel information extraction module (MCI) and the global-local attention based on context information module (GLA-CI). MCI comprehensively extracts and utilizes multi-scale 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 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 paper can be found at https://github.com/falian0527/MCI-GLA.