Identification of Low-Value Defects in Infrared Images of Porcelain Insulators Based on STCE-YOLO Algorithm

Insulators, as a key component of the power system, their low-value defect detection is of great significance to ensure the safe and stable operation of the power system. However, traditional detection methods have many shortcomings in the face of a complex environment and small target recognition. To solve the above problems, this paper optimizes the small target and complex environment problems in the low-value defect recognition of insulator infrared images, and proposes the STCE-YOLO algorithm: based on YOLOv8, the deformable large kernel attention is used to improve the detection ability of small targets; then the cross-modal contextual feature module is applied to Integrate the features of different scales to reduce the computation of the model. And the multiple attention mechanism improved to the third generation of variability convolution is used to detect the head to improve the accuracy of the algorithm's target localization. Finally, the SIoU loss function is employed to further enhance performance in complex scenes containing small targets. Experimental validation has shown that the STCE-YOLO algorithm proposed in this paper achieves an average improvement of 7.64% in mAP compared to the original YOLOv8, with GFLOPs reduced from 8.1 to 7.7. This meets the requirements for identifying low-value defects in small target insulators. Furthermore, ablation and comparative experiments have demonstrated the effectiveness and superiority of the proposed algorithm.