Detecting visual anomalies in an industrial environment: Unsupervised methods put to the test on the AutoVI dataset

This publication has 24 references indexed in Scilit:

• Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization
  International Journal of Computer Vision, 2022
• A survey on anomaly detection for technical systems using LSTM networks
  Computers in Industry, 2021
• Mixed supervision for surface-defect detection: From weakly to fully supervised learning
  Computers in Industry, 2021
• Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image Data
  Integrating Materials and Manufacturing Innovation, 2021
• The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
  International Journal of Computer Vision, 2021
• Segmentation-based deep-learning approach for surface-defect detection
  Journal of Intelligent Manufacturing, 2019
• A fast and robust convolutional neural network-based defect detection model in product quality control
  The International Journal of Advanced Manufacturing Technology, 2017
• Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection
  CIRP Annals, 2016
• ImageNet Large Scale Visual Recognition Challenge
  International Journal of Computer Vision, 2015
• A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects
  Applied Surface Science, 2013