A novel calibration approach to structured light 3D vision inspection
A novel calibration approach to structured light 3D vision inspection
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DOI：
10.1016/s0030-3992(02)00031-2
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发表时间：
2002-07-01
影响因子：
5
通讯作者：
Wei, ZZ
中科院分区：
文献类型：
--
作者：
Zhang, GJ;Wei, ZZ
结构光三维视觉检测是各种三维表面仿形技术的常用方法。本文提出了一种新的方法，以产生足够的高精度的结构光三维视觉标定点。该方法是基于一个灵活的校准目标，由光电瞄准装置和三维平移平台。提出了一种改进的BP神经网络算法，并成功地应用于结构光三维视觉检测的标定中。最后，利用标定点和BP神经网络的改进算法，建立了最佳网络结构。最佳BP网络结构的训练精度为0.083 mm，测试精度为0.128 mm。（C）2002 Elsevier Science Ltd.版权所有。
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. (C) 2002 Elsevier Science Ltd. All rights reserved.