A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface

• Main Authors: Liming Zhao, Fangfang Li, Yi Zhang, Xiaodong Xu, Hong Xiao, Yang Feng
• Format: Article
• Language: English
• Published: MDPI AG 2020-02-01
• Series: Sensors
• Subjects: continuous casting; surface defects; 3d imaging; neural network; deep learning; defect detection
• Online Access: https://www.mdpi.com/1424-8220/20/4/980

To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.