Real-time segmentation network for accurate weld detection in large weldments

Aiming at the defects of inaccurate weld extraction and high matching error rate in automatic welding system of large weldments currently. We propose a multi task detection model based on CNN architecture, which integrates the semantic segmentation technology required for weldment merging as well as the edge detection technology needed for weld matching. In particular, for the purpose of predicting smoother edges and welds, we carefully construct a new segment head, which adopts the sub-pixel convolution technology for up-sampling. Furthermore, a joint optimization loss function is explored to alleviate the imbalance of category distribution in large-scale weldment datasets. To verify the effectiveness of the model, abundant groups of data are collected for training and testing. The experimental results indicate that the proposed method has achieved the optimal trade-off between detection accuracy (83.35% mIoU, 95.15% F-score of welds and edges) as well as speed (74FPS) on a 2080Ti GPU compared with other state-of-the-arts, which greatly improves the robustness of the automatic welding system for large weldments.