As a core technology in 3D measurement, laser stripe center extraction is widely applied in industrial inspection, robot navigation, and biomedicine. However, traditional methods struggle to balance denoising effectiveness and positioning accuracy when handling complex noise and non-uniform width stripes. To address this bottleneck, this paper proposes a denoising-adaptive weighted average width stripe center extraction algorithm based on an improved Hessian Matrix, integrating deep learning with traditional image processing for high-precision extraction. A U-Net++ denoising network with a spatial attention module is designed to focus on stripe regions, supplemented by a distance-aware mechanism that dynamically adjusts denoising intensity based on pixel-stripe distance. For center extraction, an improved Hessian Matrix algorithm is proposed, incorporating a curvature-adaptive FIR filter and adaptive weighted average width calculation to adapt to stripe morphology changes. Experimental results show the algorithm outperforms comparative methods, achieving 35.26 dB (PSNR), 0.962 (SSIM), and 6.14 (RMSE) in denoising. Under 200 μs, 500 μs, 1000 μs, and 1500 μs exposure conditions, the absolute radius errors are reduced to 0.2052 mm, 0.1743 mm, 0.0268 mm, and 0.0281 mm, respectively, verifying its reliability and stability in practical applications.
Liu et al. (Wed,) studied this question.