TY - GEN
T1 - Visual inspection of coatings in hydrogen fuel cells
AU - Li, Chenjie
AU - Zhou, Mian
AU - Pan, Weiwei
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2025/1/31
Y1 - 2025/1/31
N2 - In industrial production, the presence or absence of defects on the surface of industrial products will have a great impact on the quality of some products. In this paper, we propose a method that combines traditional image recognition and deep learning-based object detection algorithms, which aims to detect whether there are defects on the coating of hydrogen fuel cells and identify the types and sizes of defects. This method integrates multiple functions for detecting different defects, such as the overall detection and identification of defects such as large area missing coating, uneven application, empty bands and dimensions that do not meet the standard, and detailed detection such as the detection of light transmission points and the detection of light-sladen areas of the coating material (referred to as dark layers in this paper). The system can be installed directly on the production line to detect defects in the coating material of hydrogen fuel cells.
AB - In industrial production, the presence or absence of defects on the surface of industrial products will have a great impact on the quality of some products. In this paper, we propose a method that combines traditional image recognition and deep learning-based object detection algorithms, which aims to detect whether there are defects on the coating of hydrogen fuel cells and identify the types and sizes of defects. This method integrates multiple functions for detecting different defects, such as the overall detection and identification of defects such as large area missing coating, uneven application, empty bands and dimensions that do not meet the standard, and detailed detection such as the detection of light transmission points and the detection of light-sladen areas of the coating material (referred to as dark layers in this paper). The system can be installed directly on the production line to detect defects in the coating material of hydrogen fuel cells.
KW - Coated material
KW - Defect detection
KW - Hydrogen fuel cells
KW - Yolo
UR - http://www.scopus.com/inward/record.url?scp=85219171334&partnerID=8YFLogxK
U2 - 10.1145/3707292.3707380
DO - 10.1145/3707292.3707380
M3 - Conference Proceeding
AN - SCOPUS:85219171334
T3 - Proceedings of 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024
SP - 298
EP - 302
BT - Proceedings of 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024
PB - Association for Computing Machinery, Inc
T2 - 3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024
Y2 - 25 October 2024 through 27 October 2024
ER -