Fig. 1. Overall workflow chart of defect detection

Fig. 2. Experiment system for laser welding defect detection

Fig. 3. Complete sample images of typical welds. (a) Sheet butt weld;(b) thick plate butt weld; (c) bead-on-plate weld

Fig. 4. Different forms of data during data preprocessing. (a) Preprocessing of point cloud HDM data; (b) RGB images of surface defects; (c) high-density point cloud data; (d) depth images including 3D profile information of defects

Fig. 5. Structural diagram of Faster R-CNN

Fig. 6. Detection results using Faster R-CNNs based on ResNet18, ResNet50, and ResNet101

Fig. 7. Statistical results of three models. (a) Loss evolution of different models; (b) point cloud detection precisions and recall rates with different models; (c) detection precisions and recall rates of defects for point clouds and RGB images with different models; (d) detection mAPs of defects for point clouds and RGB images with different models; (e) testing time of different models

Fig. 8. Typical false negative test results of Faster R-CNN model based on ResNet50

Fig. 9. Measurement process of defect sizes. (a) RGB images; (b) point clouds; (c) depth gray images; (d) threshold segmentation; (e) locating defect areas; (f) defect feature size measurement

Fig. 10. Relative errors of defect measurement results