TY - GEN
T1 - 3D Point Cloud-Based Lithium Battery Surface Defects Detection Using Region Growing Proposal Approach
AU - Rehman, Zia Ur
AU - Wang, Xin
AU - Alsumeri, Abdulrahman Abdo Ali
AU - Khan, Malak Abid Ali
AU - Ma, Hongbin
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery.
AB - Detecting the lithium battery surface defects is a difficult task due to the illumination reflection from the surface. To overcome the issue related to labeling and training big data by using 2D techniques, a 3D point cloud-based technique has been proposed in this paper. The 3D point cloud-based defect detection of lithium batteries used feature-based techniques to downscale the point clouds to reduce the computational cost, extracting the normals of the points and calculating their differences to detect the defects of the battery which assure the quality of the product. This paper offers a novel strategy using 3D point clouds to get beyond the labeling and training challenges involved with conventional 2D approaches. This 3D point cloud-based approach for lithium battery fault identification makes use of feature-based methods to improve the point cloud data and lessen the computing burden. In our work, the experiments show that the feature-based technique precisely detects the affected surface of the battery.
KW - 3D point cloud
KW - Defects detection
KW - Region growing proposal
UR - http://www.scopus.com/inward/record.url?scp=85176911896&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7590-7_1
DO - 10.1007/978-981-99-7590-7_1
M3 - Conference contribution
AN - SCOPUS:85176911896
SN - 9789819975891
T3 - Communications in Computer and Information Science
SP - 3
EP - 14
BT - Advanced Computational Intelligence and Intelligent Informatics - 8th International Workshop, IWACIII 2023, Proceedings
A2 - Xin, Bin
A2 - Kubota, Naoyuki
A2 - Chen, Kewei
A2 - Dong, Fangyan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2023
Y2 - 3 November 2023 through 5 November 2023
ER -