A refined small-target model based on YOLOv5s for accurate insulator defect detection in complex environments

Abstract

Insulators are critical components that directly determine the secure operation of power systems. Prolonged exposure to harsh outdoor environments renders them susceptible to typical defects, including breakage and missing components. Insulator inspection images captured in field scenarios are commonly characterized by cluttered backgrounds and tiny target sizes, which pose substantial obstacles to accurate defect identification. These obstacles include excessive model parameter counts, unsatisfactory detection precision, and elevated rates of missed detections and false alarms. To solve these problems, a lightweight small-target detection model is proposed tailored for insulator defects, which is improved based on YOLOv5s. First, several C3 modules in the backbone of the baseline YOLOv5s are replaced by Hybrid Attention Transformer stages. These blocks exploit the complementary strengths of window-based self-attention and channel-wise attention, effectively enhancing the model’s capacity to extract discriminative features for small-target insulator defects (STID). Second, the Slim-Neck architecture is employed to guarantee the model’s ability to capture multi-scale features of insulator defects. Third, the directionally sensitive SIoU loss function is utilized to improve the precision of bounding box localization. Experimental results illustrate that STID-YOLO yields a 12.2% absolute gain in mAP@0.5 while cutting model parameters by 48.8% and GFLOPs by 56.3% relative to the baseline YOLOv5s. Consequently, the proposed model offers a promising solution for accurate insulator defect detection in complex environments with favorable detection efficiency.