A feature fusion-based line structured light 3-D imaging method

To address the challenges of uneven illumination, background noise, and low contrast in laser stripe center extraction in industrial environments such as measurement, this paper proposes a line laser 3D measurement method based on feature fusion. First, a multi-feature fusion detection model is constructed to obtain the brightness feature map and region-enhanced feature map of the laser stripe. Then, wavelet transform is used to fuse the brightness feature map and the region-enhanced feature map, followed by segmentation of the fused feature map using an adaptive maximum entropy method. Next, a gray-gravity method optimized by gradient direction and curvature is employed to determine the initial center points of the laser stripe. Finally, adaptive segmentation based on neighborhood differences is performed, and polynomial fitting is applied to the segmented stripes to obtain the final laser stripe centers. Experimental results show that, under a noise variance of 0.3, the maximum error in laser stripe extraction is less than 1.07 pixels, the width error between different rows is less than 0.24 mm, and the mean error of repeatability extraction is less than 0.13 mm.