Lightweight Infrared Image Fault Region Segmentation Method for Substation Equipment Based on YOLOv8

Existing deep learning models struggle to balance accuracy and efficiency in segmenting fault areas within infrared images of substation equipment,which typically feature irregular shapes,small targets,and blurred edges.To address this, we propose a lightweight segmentation model based on an improved YOLOv8 framework.For model lightweighting,MobileNetV3 serves as the backbone, significantly reducing parameters and computational complexity through depthwise separable convolutions combined with an attention mechanism,while maintaining competent feature extraction.To enhance multi-scale irregular target perception,a Semantic Detail Injection Module is introduced, enabling effective fusion of high-level semantics with low-level detail features.Additionally,an Upsampling Inverted Bottleneck Fusion Structure is developed to address inherent infrared edge blurring,strengthening boundary delineation of fault areas.A composite loss function integrating Focal Loss and Dice Loss is employed to alleviate class imbalance and enhance robustness in complex backgrounds.Experimental results show the proposed method improves mAP by 2.38% over original YOLOv8 while reducing parameters by 26.46%.Benchmarking against four mainstream segmentation algorithms confirms its superior accuracy and computational efficiency, demonstrating strong potential for intelligent power equipment maintenance applications.