Next Article in Journal
Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System
Previous Article in Journal
Statistical Analysis of Noise Propagation Effect for Mixed RF/FSO AF Relaying Application in Wireless Sensor Networks
Previous Article in Special Issue
Automatic Fabric Defect Detection Using Cascaded Mixed Feature Pyramid with Guided Localization
Open AccessArticle

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

Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 980; https://doi.org/10.3390/s20040980
Received: 27 December 2019 / Revised: 3 February 2020 / Accepted: 7 February 2020 / Published: 12 February 2020
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. View Full-Text
Keywords: continuous casting; surface defects; 3D imaging; neural network; deep learning; defect detection continuous casting; surface defects; 3D imaging; neural network; deep learning; defect detection
Show Figures

Figure 1

MDPI and ACS Style

Zhao, L.; Li, F.; Zhang, Y.; Xu, X.; Xiao, H.; Feng, Y. A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface. Sensors 2020, 20, 980.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop