Infrared Image Detection and Recognition of Substation Electrical Equipment Based on Improved YOLOv8

Abstract

To address the challenges associated with lightweight design and small object detection in infrared imaging for substation electrical equipment, this paper introduces an enhanced YOLOv8_Adv network model. This model builds on YOLOv8 through several strategic improvements. The backbone network incorporates PConv and FasterNet modules to substantially reduce the computational load and memory usage, thereby achieving model lightweighting. In the neck layer, GSConv and VoVGSCSP modules are utilized for multi-stage, multi-feature map fusion, complemented by the integration of the EMA attention mechanism to improve feature extraction. Additionally, a specialized detection layer for small objects is added to the head of the network, enhancing the model’s performance in detecting small infrared targets. Experimental results demonstrate that YOLOv8_Adv achieves a 4.1% increase in mAP@0.5 compared to the baseline YOLOv8n. It also outperforms five existing baseline models, with the highest accuracy of 98.7%, and it reduces the computational complexity by 18.5%, thereby validating the effectiveness of the YOLOv8_Adv model. Furthermore, the effectiveness of the model in detecting small targets in infrared images makes it suitable for use in areas such as infrared surveillance, military target detection, and wildlife monitoring.