A centerline extraction method for laser stripes with geometric discontinuous profiles based on physics-conditioned generative adversarial networks
Year: 2026
Type: article
Abstract: To address the accuracy degradation in centerline extraction for laser stripes with geometric discontinuities, an improved centerline extraction method integrating both contour segmentation and geometric decomposition based on physics-conditioned generative adversarial network (pc-GAN) was proposed. First, the causes of accuracy degradation at curvature discontinuities were analyzed, with an intensity distribution model and a mixed noise distribution model established to characterize the laser stripes. Secondly, the Gaussian-Poisson mixed noise distribution was taken as the constraint for pc-GAN to segment laser stripes with complex profiles, further decompose the laser stripes into geometric primitives according to the predicted inflection point probability map. Then, a data simulation algorithm was developed based on the imaging mechanism of line structured light, combined with real data, a semi-physical dataset was constructed. Finally, experimental results demonstrated that the proposed network achieved a 2.42% increase in IoU and a 0.037 reduction in RMSE compared with existing networks, which improved the reconstruction accuracy of complex profiles by a factor of 3.43. And the peak to valley standard deviation of 2.67 μm when applied in PCB inspection.
Source: Optics & Laser Technology
Institution: Shanghai Jiao Tong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,230
OpenAlex ID: W7222806399