TY - JOUR
T1 - Surface defect detection methods for industrial products with imbalanced samples
T2 - a review of progress in the 2020s
AU - Bai, Dongxu
AU - Li, Gongfa
AU - Jiang, Du
AU - Yun, Juntong
AU - Tao, Bo
AU - Jiang, Guozhang
AU - Sun, Ying
AU - Ju, Zhaojie
N1 - Funding Information:
This work was supported by grants of the National Natural Science Foundation of China (Grant Nos. 51575407 , 51505349 ); "The 14th Five Year Plan" Hubei Provincial advantaged characteristic disciplines (groups) project of Wuhan University of Science and Technology ( 2023C0401 ); the China Scholarship Council (No. 202308420243 ).
Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2024/4/1
Y1 - 2024/4/1
N2 - Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample distribution, machine learning and deep learning algorithms preferentially learn features from the majority classes, potentially leading to inaccurate results. Addressing the issue of sample imbalance has thus emerged as a critical area of research within the field of industrial intelligent manufacturing. This paper discusses the imbalanced sample problem of industrial product surface defect detection algorithms, and proposes the existence of "four imbalances and two uncertainties". It also summarizes the industrial product surface dataset and innovatively adds the imbalance rate comparison to the dataset. In this study, data re-sampling, data expansion, feature extraction and identification, and re-weighting of category weights are elaborated at the level of data and algorithm respectively. Additionally, the paper explores prospective directions for future research, including supervised and unsupervised learning, transfer learning, anomaly detection, quality prediction, and future challenges. It is hoped to lay a solid foundation for the more far-reaching development of smart manufacturing and surface defect detection methods. And provide some directions for the research of sample imbalance and long-tail problems.
AB - Industrial products typically lack defects in smart manufacturing systems, which leads to an extremely imbalanced task of recognizing surface defects. With this imbalanced sample distribution, machine learning and deep learning algorithms preferentially learn features from the majority classes, potentially leading to inaccurate results. Addressing the issue of sample imbalance has thus emerged as a critical area of research within the field of industrial intelligent manufacturing. This paper discusses the imbalanced sample problem of industrial product surface defect detection algorithms, and proposes the existence of "four imbalances and two uncertainties". It also summarizes the industrial product surface dataset and innovatively adds the imbalance rate comparison to the dataset. In this study, data re-sampling, data expansion, feature extraction and identification, and re-weighting of category weights are elaborated at the level of data and algorithm respectively. Additionally, the paper explores prospective directions for future research, including supervised and unsupervised learning, transfer learning, anomaly detection, quality prediction, and future challenges. It is hoped to lay a solid foundation for the more far-reaching development of smart manufacturing and surface defect detection methods. And provide some directions for the research of sample imbalance and long-tail problems.
KW - Artificial intelligence
KW - Imbalanced sample
KW - Industrial product
KW - Intelligent defect detection
KW - Smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85180536404&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2023.107697
DO - 10.1016/j.engappai.2023.107697
M3 - Article
AN - SCOPUS:85180536404
SN - 0952-1976
VL - 130
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 107697
ER -