Automatic weld type classification, tacked spot recognition and weld ROI determination for robotic welding based on modified YOLOv5. (June 2023)

• Record Type: Journal Article
• Authors: Chen, Shengfeng; Yang, Dezhi; Liu, Jian; Tian, Qi; Zhou, Feitao
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 81(2023)
• Publication Date: 2023-06
• Subjects: Weld type classification -- Tacked spot recognition -- Weld ROI determination -- Robotic welding -- Yolov5 -- Intelligent welding robot; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102490
• Languages: English
• ISSNs: 0736-5845

Abstract

Highlights: An automatic weld type classification, tacked spot recognition and weld ROI determination for robotic welding based on modified YOLOv5 is presented. The presented method is accurate, fast, and robust. The presented SCIOU localization loss can reduce the maximum center component bias between the predicted box and ground truth. The presented self-template border padding method improves the generalization ability of the model. Abstract: Automatic and fast weld type classification, tacked spot recognition and weld ROI (region of interest) determination are key links for intelligent welding robot, because they directly affect the seam tracking and welding parameters such as current, voltage and torch inclination. Nevertheless, there are few studies on weld type classification, tacked spot recognition, and weld ROI determination. Considering the fast inference speed of YOLOv5, an automatic weld type classification, tacked spot recognition and weld ROI determination based on modified YOLOv5 is presented in this paper. First, the detection requirements of weld type classification, tacked spot recognition and weld ROI determination are transformed into a unified target localization task to improve the inference speed, so the three results can be obtained through single inference; the next, to improve the localization accuracy of weld ROI, the center component bias between the predicted box and ground truth is added to the original CIOU localization loss function; then, a weighted classification loss function is used to reduce the false positives in fillet and groove welds; finally, a self-template method for padding image border is presented to improve the generalization ability of the trained model. Experimental results show that: the presented method reaches 100% precision, 100% recall, 0.91 mean intersection-over-unio, 2.41 pixels center component bias of determined weld ROI and 18 ms inference times in the original size images; the center component bias of determined weld ROI is reduced from 2.38 pixels to 2.18 pixels by adding the center component bias loss to CIOU function in the padded images; the weighted classification loss function reduced the false positives in fillet and groove welds; compared with the default gray border padding method, the model trained by using self-template padding method reduced the center component bias of the determined weld ROI from 2.73 pixels to 2.41 pixels in the original size images. Moreover, when Ref. Chen et al. (2022) positions the welding seam coordinates in the weld ROI determined by the presented method, the recall is improved from 0.96 to 0.98, and the computation time is reduced from 180 ms to 48 ms.