Congratulations to our MSAAI graduate, Haiyu He, whose master’s thesis has been accepted by the international journal Artificial Intelligence and Robotics Research (https://www.hanspub.org/journal/AIRR.html)!
- Haiyu He, Youhai Peng, Yulin Zhu(通訊作者), Wai-Lun Lo. (May. 2025). Research on the Application of Machine Learning Algorithms for Steel Defect Classification in the Perspective of Smart Infrastructure. Artificial Intelligence and Robotics Research.
Paper Abstract
This paper investigates the problem of steel defects classification in smart infrastructure based on the UCI Strip Steel Defects Public Database of the National Institute of Standards and Technology and the Steel Industry Association. The samples are derived from 1941 samples from 3 North American steel mills from 2018~2021, with a total of 27 types of features. Six algorithms are selected and data preprocessing, feature engineering, and hyper-parameter tuning strategies are adopted to establish an efficient and accurate intelligent classification scheme for steel defects. The innovations include: building a fusion model of multiple algorithms; designing a feature classification screening and tuning scheme; adopting SMOTE to solve the problem of unbalanced samples; and setting up a complete experimental evaluation system. The results show that the precision and recall of LightGBM and neural network are more than 96%. Ablation experiments and parameter sensitivity analysis demonstrate the importance of these methods for feature selection and hyperparameterization. Subsequent research will expand the number of collected samples and try to combine new technologies such as deep learning and computer vision to make the model more pervasive, robust, and higher detection accuracy, and promote the intelligent development of smart infrastructure more widely.
Photos from the paper
Detection results of some pictures and videos.
