Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries

Authors:
raw_author_name: Qing Zhao
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Yan Wang
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Yuxuan Lin
raw_affiliation_string: School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China
raw_author_name: Shaoqi Yan
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Wei Song
raw_affiliation_string: College of Information Technology, Shanghai Ocean University, Shanghai, 201306, China
raw_author_name: Boyang Wang
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Jun Huang
raw_affiliation_string: School of Computer Science and Technology, Anhui University of Technology, Maanshan, 243032, China
raw_author_name: Yang Chang
raw_affiliation_string: School of Information Science and Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Lizhe Qi
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Wenqiang Zhang
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China; School of Computer Science and Technology, Fudan University, Shanghai, 200433, China

Abstract: Large-scale sensor and data acquisition systems, integrated with deep learning methodologies, play a pivotal role in enhancing the sustainability and security of smart city environments, exemplifying the critical significance of anomaly detection techniques. Anomaly detection in complex industrial scenarios presents various challenges, such as intricate working environments, limited anomaly samples, and lack of a priori information. Unsupervised anomaly detection based on knowledge distillation enables anomaly detection using only normal samples. However, the similarity in structure between teacher and student models, along with identical input data flow, hampers accurate anomaly detection and localization. To address these issues, we propose MNMC, an unsupervised anomaly detection model consisting of a mixed noise generation module emulating real defects, a mutual constraint module, and an anomaly segmentation module. Firstly, to enhance the student network’s ability to learn robust features, we construct a hybrid noise model comprising dead-leaves noise and perlin noise. This generates features with structural texture and distributional characteristics closer to real anomalies. Secondly, we design a mutual constraint framework to further improve the learning ability of the student network for normal features by constraining representations containing only a single noise. Lastly, for the detection of anomalies at different scales, we propose a new evaluation metric based on equal importance of normal and anomalous regions. Through ablation experiments, we demonstrate the effectiveness of the simulated real defect generation module and the mutual constraints module. Performance experiments on the MVTec dataset show that our method achieves competitive results compared to the current state-of-the-art anomaly detection methods.