Machine Vision-Based Object Detection Strategy for Weld Area

Abstract

For the noisy industrial environment, the welded parts will have different types of defects in the weld area during the welding process, which need to be polished, and there are disadvantages such as low efficiency and high labor intensity when polishing manually; machine vision is used to automate the polishing and achieve continuous and efficient work. In this study, the Faster R-CNN object detection algorithm of two-stage is used to investigate the relationship between flops and the number of network parameters on the model by using a V-shaped welded thick plate as the research object and establishing the workpiece dataset with different lighting and angles, using six regional candidate networks for migration learning, comparing the convergence degree of different Batch and Mini-Batch on the model, and exploring the relationship between flops and the number of network parameters on the model. The optimal learning rate is selected for training to form a weld area object detection network based on the weld plate workpiece under few samples. The study shows that the VGG16 model is the best in weld seam area recognition with 91.68% average accuracy and 25.02 ms average detection time in the validation set, which can effectively identify weld seam areas in various industrial environments and provide location information for subsequent automatic grinding of robotic arms.