A Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry
- 25 September 2022
- book chapter
- Published by Springer Nature
- p. 1527-1538
Abstract
No abstract availableKeywords
All Related Versions
This publication has 18 references indexed in Scilit:
- Unsupervised Machine Learning Via Transfer Learning and k-Means Clustering to Classify Materials Image DataIntegrating Materials and Manufacturing Innovation, 2021
- The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly DetectionInternational Journal of Computer Vision, 2021
- Attention Guided Anomaly Localization in Images2020
- A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection2020
- Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard IndexIEEE Transactions on Medical Imaging, 2020
- A robust weakly supervised learning of deep Conv-Nets for surface defect inspectionNeural Computing and Applications, 2020
- MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection2019
- Segmentation-based deep-learning approach for surface-defect detectionJournal of Intelligent Manufacturing, 2019
- A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defectsApplied Surface Science, 2013
- Learning defect classifiers for visual inspection images by neuro-evolution using weakly labelled training data2008