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

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ID: 96008
2020
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Abstract
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.
Reference Key
zhao2020asensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhao, Liming;Li, Fangfang;Zhang, Yi;Xu, Xiaodong;Xiao, Hong;Feng, Yang;
Journal sensors
Year 2020
DOI
E980
URL
Keywords
Deep learning Neural network Defect detection continuous casting surface defects 3d imaging

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