Improvement of deformable DETR model for insulator defect classification detection method

Abstract In response to challenges faced by traditional detection methods such as image blurring, scarcity of insulator defect datasets, and insulators as small targets, we propose an improved Deformable DETR network based on the DETR defect detection algorithm. This network model accurately identifies the position information of insulators and segments the insulators on the insulator string. To address the classification problem after defect detection, we introduce a fused insulator defect classifier with a self-attention mechanism built behind the Deformable DETR model. The detected insulators are classified into three categories. Due to the limited dataset of damaged insulators, corresponding loss functions are set to address the issue of sample imbalance, thereby increasing the model’s focus on damaged insulators. Experimental results demonstrate an accuracy of 97.5% on the test set, highlighting the network’s strong generalization ability.