TY - GEN
T1 - Surface Defect Detection
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
AU - Yang, Junqing
AU - Wu, Chengzhangzheng
AU - Liu, Taimingwang
AU - Ateeq, Muhammad
AU - Hafizh, Hadyan
AU - Fakhri Ab. Nasir, Ahmad
AU - Abdul Majeed, Anwar P.P.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Surface defects in manufacturing processes pose significant challenges, affecting product quality and safety. Traditional labour-based inspection is deemed to be ineffective and has led the shift to computer vision-based solutions and to a certain extent, the employment of artificial intelligence. In the present study, we leverage the capability of a pre-trained convolutional neural networks model, i.e. VGG19, in extracting the features from a set of surface defect dataset that comprises six unique defect categories. The ability of different machine learning models, namely Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbour (kNN) and Support Vector Machine (SVM), to classify the defects was investigated. It was demonstrated from the study that the VGG-19 + LR combination is the optimal pipeline. This study suggests that the feature-based transfer learning approach is an attractive approach to be employed for surface defect detection.
AB - Surface defects in manufacturing processes pose significant challenges, affecting product quality and safety. Traditional labour-based inspection is deemed to be ineffective and has led the shift to computer vision-based solutions and to a certain extent, the employment of artificial intelligence. In the present study, we leverage the capability of a pre-trained convolutional neural networks model, i.e. VGG19, in extracting the features from a set of surface defect dataset that comprises six unique defect categories. The ability of different machine learning models, namely Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbour (kNN) and Support Vector Machine (SVM), to classify the defects was investigated. It was demonstrated from the study that the VGG-19 + LR combination is the optimal pipeline. This study suggests that the feature-based transfer learning approach is an attractive approach to be employed for surface defect detection.
KW - Deep learning
KW - Feature extraction
KW - Surface defect detection
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85211343730&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70687-5_6
DO - 10.1007/978-3-031-70687-5_6
M3 - Conference Proceeding
AN - SCOPUS:85211343730
SN - 9783031706868
T3 - Lecture Notes in Networks and Systems
SP - 58
EP - 65
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P. P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 December 2023 through 8 December 2023
ER -