Open Access
ARTICLE
Classification-Detection of Metal Surfaces under Lower Edge Sharpness Using a Deep Learning-Based Approach Combined with an Enhanced LoG Operator
Hong Zhang1,*, Jiaming Zhou1, Qi Wang1, Chengxi Zhu1, Haijian Shao2
1
School of Electrical Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China
2
Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA
* Corresponding Author: Hong Zhang. Email:
Computer Modeling in Engineering & Sciences 2023, 137(2), 1551-1572. https://doi.org/10.32604/cmes.2023.027035
Received 11 October 2022; Accepted 06 February 2023; Issue published 26 June 2023
Abstract
Metal flat surface in-line surface defect detection is notoriously difficult due to obstacles such as high surface
reflectivity, pseudo-defect interference, and random elastic deformation. This study evaluates the approach for
detecting scratches on a metal surface in order to address a problem in the detection process. This paper proposes
an improved Gauss-Laplace (LoG) operator combined with a deep learning technique for metal surface scratch
identification in order to solve the difficulties that it is challenging to reduce noise and that the edges are unclear
when utilizing existing edge detection algorithms. In the process of scratch identification, it is challenging to
differentiate between the scratch edge and the interference edge. Therefore, local texture screening is utilized by
deep learning techniques that evaluate and identify scratch edges and interference edges based on the local texture
characteristics of scratches. Experiments have proven that by combining the improved LoG operator with a deep
learning strategy, it is able to effectively detect image edges, distinguish between scratch edges and interference
edges, and identify clear scratch information. Experiments based on the six categories of meta scratches indicate
that the proposed method has achieved rolled-in crazing (100%), inclusion (94.4%), patches (100%), pitted (100%),
rolled (100%), and scratches (100%), respectively.
Keywords
Cite This Article
APA Style
Zhang, H., Zhou, J., Wang, Q., Zhu, C., Shao, H. (2023). Classification-detection of metal surfaces under lower edge sharpness using a deep learning-based approach combined with an enhanced log operator. Computer Modeling in Engineering & Sciences, 137(2), 1551-1572. https://doi.org/10.32604/cmes.2023.027035
Vancouver Style
Zhang H, Zhou J, Wang Q, Zhu C, Shao H. Classification-detection of metal surfaces under lower edge sharpness using a deep learning-based approach combined with an enhanced log operator. Comp Model Eng. 2023;137(2):1551-1572 https://doi.org/10.32604/cmes.2023.027035
IEEE Style
H. Zhang, J. Zhou, Q. Wang, C. Zhu, and H. Shao "Classification-Detection of Metal Surfaces under Lower Edge Sharpness Using a Deep Learning-Based Approach Combined with an Enhanced LoG Operator," Comp. Model. Eng., vol. 137, no. 2, pp. 1551-1572. 2023. https://doi.org/10.32604/cmes.2023.027035