End-to-end industrial small-scale defect recognition based on real-time detection transformer

Industrial defect recognition is a key part of product quality control. Small-scale defect recognition has become a difficult task in defect detection due to the weak and easy loss of recognizable features. To address this problem, this paper proposes an end-to-end small-scale defect detection algorithm based on RT-DETR (Real-Time DEtection TRansformer) model. First, we feed a shallow texture feature layer into the efficient hybrid encoder and optimize the cross-scale feature fusion module with RepC3 block to extract richer texture features for small-scale defects; Second, we utilize IoU-aware Query Selection for feature screening to ensure the reliability of the features input to the decoder for prediction; Finally, we conduct comparison experiments on the self-made WM-FSD dataset and HRIPCB dataset, and the experimental results demonstrate that our proposed method for small-scale defect recognition in different scenarios has obvious advantages in terms of precision, recall, and average precision.