To evaluate defects on the surface of the materials at the 3D level accurately and quantitatively, a 3D surface defect detection system based on stereo vision is presented, which can extract the precise 3D defect features of the detected object. The proposed detection system consists of two image capture modules and a turntable to capture the complete 3D information and color texture information from the object surface. More precisely, each image capture module is a binocular stereo vision system containing two monochrome cameras, a color camera, and a speckle projector which is used to reconstruct the 3D point clouds of the object surface based on stereo digital image correlation (stereo-DIC). Furthermore, a point-image mapping relationship between the reconstructed 3D object points and the color images is established. Eventually, the 3D characteristic parameters of defects are calculated by the corresponding 3D point cloud of the defect area obtained by segmenting the defect area using the image segmentation and point cloud segmentation algorithms according to this point-image mapping relationship. A convolutional neural network named DenseNets is employed to identify defect types intelligently. A high-precision multi-camera calibration method based on close-range photogrammetry is applied to ensure system detection accuracy in the proposed system. The experimental results demonstrate that the system has higher accuracy and better performance in system calibration, 3D reconstruction, and defect feature calculation.

为了在3D水平上准确定量地评估材料表面的缺陷，提出了一种基于立体视觉的3D表面缺陷检测系统，该系统可以提取被检测物体的精确3D缺陷特征。所提出的检测系统包括两个图像捕获模块和一个转盘，以从物体表面捕获完整的3D信息和颜色纹理信息。更准确地说，每个图像捕获模块都是一个双目立体视觉系统，包含两个单色相机，一个彩色相机和一个散斑投影仪，用于基于立体数字图像相关性（stereo-DIC）重建对象表面的3D点云。 。此外，建立了重构的3D对象点与彩色图像之间的点图像映射关系。最终，缺陷的3D特征参数由缺陷区域的对应3D点云计算得到，该缺陷区域是通过根据该点-图像映射关系使用图像分割和点云分割算法对缺陷区域进行分割而获得的。使用名为DenseNets的卷积神经网络来智能地识别缺陷类型。为了保证系统检测的准确性，采用了一种基于近距离摄影测量的高精度多摄像机标定方法。实验结果表明，该系统在系统校准，3D重建和缺陷特征计算方面具有更高的准确性和更好的性能。