Title: Research on ceramic tile defect detection based on YOLOv3

Authors: Gongfa Li; Xin Liu; Bo Tao; Du Jiang; Fei Zeng; Shuang Xu

Addresses: Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China ' Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei, China

Abstract: Artificial intelligence is a technology that studies, simulates and expands human intelligence theory and related methods, it is the direction of modern and future science and technology development. The teaching methods of artificial intelligence courses are supposed to be different from the traditional teaching methods, but the actual investigation finds that there are still some problems in the artificial intelligence course, such as the single teaching mode, the low enthusiasm of students for studying, and the poor practical ability of students. In order to solve these issues, this paper applies project teaching methods to an artificial intelligence course, through a specific tile defect detection project to analyse. YOLOv3 algorithm is used to detect six kinds of tile defects, and the experimental results are analysed.

Keywords: defect detection; artificial intelligence; YOLOv3 algorithm; project-based teaching.

DOI: 10.1504/IJWMC.2021.120013

International Journal of Wireless and Mobile Computing, 2021 Vol.21 No.2, pp.128 - 133

Received: 12 Jun 2021
Accepted: 15 Jul 2021
Published online: 04 Jan 2022 *

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