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A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface
- Source :
- Sensors, Vol 20, Iss 4, p 980 (2020), Sensors (Basel, Switzerland), Sensors, Volume 20, Issue 4
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
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&rsquo<br />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.
- Subjects :
- Surface (mathematics)
Computer science
neural network
defect detection
0211 other engineering and technologies
02 engineering and technology
lcsh:Chemical technology
01 natural sciences
Biochemistry
Article
surface defects
Analytical Chemistry
Image (mathematics)
Computer vision
lcsh:TP1-1185
Electrical and Electronic Engineering
Instrumentation
021102 mining & metallurgy
3d imaging
business.industry
Deep learning
010401 analytical chemistry
Inspection method
Process (computing)
deep learning
continuous casting
Atomic and Molecular Physics, and Optics
0104 chemical sciences
Continuous casting
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 20
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Sensors
- Accession number :
- edsair.doi.dedup.....d82faf841a03e07b6f26b43050cc6f58