@Article{cmes.2023.027035,
AUTHOR = {Hong Zhang, Jiaming Zhou, Qi Wang, Chengxi Zhu, Haijian Shao},
TITLE = {Classification-Detection of Metal Surfaces under Lower Edge Sharpness Using a Deep Learning-Based Approach Combined with an Enhanced LoG Operator},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {1551--1572},
URL = {http://www.techscience.com/CMES/v137n2/53347},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.027035}
}