Multi-source Image Fusion and Improved Fault Recognition of Small Targets in Electrical Equipment by Improved YOLOv8

To solve the problem of the image target being small and susceptible to false detection caused by complex backgrounds and shadow occlusion interference in the inspection of electrical equipment, this study proposes an image fault detection algorithm for electrical equipment based on improved YOLOv8 based on image fusion of the target image. To reduce false detections and missed detections of small target faults in complex backgrounds, the recursive gated convolution of a High-Order Recursive Network (HorNet) was used to design a convolutional block with customized components (CHC), which replaces the original cross-stage partial fusion with two convolution (C2F) modules in the network, strengthens the spatial information of inter-neighborhood features, and introduces an Efficient Multi-Scale Attention (EMA) mechanism in the backbone network to capture richer detailed information and improve the feature extraction ability of the model. The Wise-Intersection over Union (WIoU) loss function was used to replace the original network loss function, effectively addressing the problems of missed detections, false detections, and overlapping target suppression. Experimental results show that, compared with the original YOLOv8n network, the improved network achieved a 19.8% increase in accuracy, a 10.8% increase in recall, and an 11.4% increase in mAP50. The proposed model demonstrates stronger feature extraction capability and higher detection accuracy for small targets and is suitable for fault detection in fused multi-source electrical equipment images.