Adaptive edge detection of molten pool based on coarse-grained regularization in restricted solution space

Edge detection is a key step in the image processing of the molten pool. In view of the sharp changes in the arc in the molten pool area, the edge detection method that relies on artificially setting the threshold is difficult to adapt to the dynamic change of the arc. This paper proposes a deep learning-based edge extraction mode of the molten pool. Firstly, pixel-level annotation and data augmentation are performed on the original molten pool image to build a dataset. Secondly, a coarse-grained regularization method in restricted solution space (CGRRSS) is proposed to enhance edge features. Finally, the proposed method is compared with the traditional methods in both quantitative and qualitative aspects. The results show that the proposed method has a higher recall of edge points, the obtained molten pool edge is more continuous and has a better suppression effect on false edges. The detection time of a single image is 6.2 ms, which can meet the needs of online monitoring.