# Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques

arXiv: https://arxiv.org/abs/2212.09352 · HTML: https://mlsanity.com/paper/2212.09352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Mixed supervision for surface-defect detection: from weakly to fully supervised learning](https://mlsanity.com/paper/2104.06064/) (2021)
- [Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey](https://mlsanity.com/paper/2207.10298/) (2022)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [One-Shot Recognition of Manufacturing Defects in Steel Surfaces](https://mlsanity.com/paper/2005.05815/) (2020)
- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)

## Built on by (most cited first, 1 of 1)

- [Human in the AI loop via xAI and Active Learning for Visual Inspection](https://mlsanity.com/paper/2307.05508/) (2023)

## Abstract (arXiv)

Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defects probably lie can be leveraged to provide cues to supervised classification models and enhance their performance. Our research shows that the best performance is achieved when training a defect detection model by providing an image and the corresponding anomaly map as input. Furthermore, such a setting provides consistent performance when framing the defect detection as a binary or multiclass classification problem and is not affected by class balancing policies. We performed the experiments on three datasets with real-world data provided by Philips Consumer Lifestyle BV.