Recursive-Selective Processing Algorithm of a Reflection Image in Structured Light Weld Pool Sensing

Processing reflection images and identifying imaging points in structured light sensing are the bases of subsequent reconstruction of a 3D weld pool surface. However, due to uneven distribution of arc light on the imaging plane and diffuse reflection of structured laser light projected onto the workpiece surface, identification could not be achieved effectively. Moreover, laser beams get scattered by metal vapor during propagation, which enlarged the sizes of some imaging points and decreased their contrast with the background, adding to difficulties in identification easily missing imaging points or mistakenly taking noises as imaging points. To solve this problem, a recursive-selective image processing algorithm was proposed. From whole to local, the algorithm used the idea of recursion to continuously select those unidentified areas for further processing. When the processed areas were returned layer by layer, all imaging points could be separated from the uneven background, with no missing or redundant points. At each layer, the main steps of the algorithm included thresholding, filtering, computing connected domains, recursive processing of large connected domains, and recovering of small, connected domains. The OTSU algorithm was used for thresholding, which automatically determined the best threshold of the target image at each layer according to its brightness characteristics. Median filtering was adopted, and it was proposed to reduce the size of the filter window by 2pixels every two layers, which would reduce the possibility of missing points and avoid invalid recursion in case the maximum recursion depth was exceeded. Finally, reflection images with unique characteristics taken at different moments after arcing were used to verify the effectiveness of the proposed algorithm, and real-time performance of the algorithm was also analyzed. The results show that the average processing time of one image is about 46ms, which meets the requirements of real-time sensing. © 2022, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.