Defect detection method for key area guided transmission line components based on knowledge distillation

Abstract

Introduction: The aim of this paper is to address the problem of the limited number of defect images for both metal tools and insulators, as well as the small range of defect features.Methods: A defect detection method for key area-guided transmission line components based on knowledge distillation is proposed. First, the PGW (Prediction-Guided Weighting) module is introduced to improve the foreground target distillation region, and the distillation range is precisely concentrated in the position of the first k feature pixels with the highest quality score in the form of a mask. The feature knowledge of defects of hardware and insulators is used as the focus for the teacher network to guide the student network. Then, the GcBlock module is used to capture the relationship between the target defects of the hardware and the transmission lines in the background, and the overall relationship information of the image is used to promote the students’ network to learn the teacher’s network perception ability of the relationship information. Finally, the classification task mask and regression task mask generated by the PGW module, combined with the overall image relationship loss, form a distillation loss function for network training to improve the accuracy of students’ network detection accuracy.Results and Discussion: The effectiveness of the proposed method is verified by using self-build metal fittings and insulator defect data sets. The experimental results show that the student network mAP_50 (Mean Average Precision at 50) in the Faster R-CNN model with the knowledge distillation algorithm added in this paper increases by 8.44%, and the RetinaNet model increases by 2.6%. The Cascade R-CNN model improved by 5.28%.