Weak Feature Defect Detection of Insulators Considering Class Boundaries and Interclass Dependencies

Insulator defects are the critical concern in intelligent inspection systems for transmission lines, typically manifesting in the forms of self-explosion, corrosion, and fouling. However, weak feature defects such as corrosion and fouling, often exhibit limited spatial extent, complex morphology, and mutual interference. Due to the limitations of the optical image acquisition sensor on the unmanned aerial vehicle (UAV), these factors can lead to unclear feature representation, large intraclass variation, and significant interclass similarity, which increase the risks of misclassification and boundary confusion in defect detection. To realize accurate detection of weak feature defects in insulators, this article proposes an improved DeepLabv3+ network fused with a boundary enhancement module (BEM) and interclass feature attention module (I-CFAM), called BEIC-DeepLabv3+. First, the BEM is proposed to enrich shallow detail information and enhance the model’s ability to understand the shape and structure of the target. Then, interclass dependencies are captured using the proposed I-CFAM to extract the semantic information of different defects more effectively. Finally, the encoder–decoder structure is used to effectively combine the shallow details and deep semantic information, enabling comprehensive feature extraction and analysis. The experimental results on the insulator defect dataset (IDD) show that the proposed method can effectively alleviate issues of misclassification and boundary confusion in weak feature defect detection of insulators, providing innovative ideas for the development of defect detection technology.