A novel calibration approach to structured light 3D vision inspection

Structured light 3D vision inspection is a commonly used method for various 3D surface profiling techniques. In this paper, a novel approach is proposed to generate the sufficient calibration points with high accuracy for structured light 3D vision. This approach is based on a flexible calibration target, composed of a photo-electrical aiming device and a 3D translation platform. An improved algorithm of back propagation (BP) neural network is also presented, and is successfully applied to the calibration of structured light 3D vision inspection. Finally, using the calibration points and the improved algorithm of BP neural network, the best network structure is established. The training accuracy for the best BP network structure is 0.083 mm, and its testing accuracy is 0.128 mm.