Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model

Abstract

This paper presents an enhanced Faster R-CNN model for detecting surface defects in resistance welding spots, improving both efficiency and accuracy for body-in-white quality monitoring. Key innovations include using high-confidence anchor boxes from the RPN network to locate welding spots, using the SmoothL1 loss function, and applying Fast R-CNN to classify detected defects. Additionally, a new pruning model is introduced, reducing unnecessary layers and parameters in the neural network, leading to faster processing times without sacrificing accuracy. Tests show that the model achieves over 90% accuracy and recall, processing each image in about 15 ms, meeting industrial requirements for welding spot inspection.

Keywords

• resistance spot welding
• surface defect detection
• deep learning model
• faster R-CNN
• small object detection