An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects

•An intelligent and automated 3D surface defect detection system for quantitative 3D estimation and feature classification of material surface defects is proposed, which transforms the defect detection from simple image detection to 3D detection.•A high-precision and high-efficiency global calibration algorithm and 3D reconstruction algorithm are proposed, which quickly reconstruct a complete 3D model of the object and establish the point-image mapping relationship between the color image and the reconstructed 3D object point.•This paper proposes a parallel image matching algorithm based on digital image correlation (DIC) method.•In the process of defect type detection, the detection image containing only the defect area is generated according to the segmentation results of the defect image, which not only improves the detection accuracy but also increases the detection speed.•This paper proposes a robust and fast algorithm for segmenting defect point clouds based on image segmentation results and point-image mapping relationships. 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 be