Liu, W.; Hu, J.; Qi, J. Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model. Preprints2024, 2024101487. https://doi.org/10.20944/preprints202410.1487.v1
APA Style
Liu, W., Hu, J., & Qi, J. (2024). Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model. Preprints. https://doi.org/10.20944/preprints202410.1487.v1
Chicago/Turabian Style
Liu, W., Jie Hu and Jin Qi. 2024 "Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model" Preprints. https://doi.org/10.20944/preprints202410.1487.v1
Abstract
Welding spot defect detection using deep learning methods provides an effective way of body-in-white quality monitoring. Based on the existing Faster R-CNN model, this paper proposed an improved faster R-CNN model for resistance welding spot surface defect inspection to improve inspection efficiency and accuracy. The model contains the following improvements. Firstly, the improved algorithm uses anchor box with higher confidence output by the RPN network to locate welding spots. When a defect is detected and the detection system is in a suspended state, the Fast R-CNN network is used to confirm the defect category and details. Secondly, a new pruning model is proposed to replace the entire backbone neural network, which unnecessary convolutional layers and connection layers are deleted, and some parameters of each hidden layer are further reduced. On the premise of ensuring detection accuracy, the parameter quantity is extremely reduced, and the speed is improved. Experiments show that the model proposed in this paper took about 15ms for one single image test, and both the detection accuracy and recall rate reached over 90% according to the test on our dataset. This deep learning model meets the requirements of welding spot defect detection.
Keywords
resistance spot welding; surface defect detection; deep learning model; Faster R-CNN; small object detection
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
