# Auto-Classifier: A Robust Defect Detector Based on an AutoML Head

arXiv: https://arxiv.org/abs/2009.01573 · HTML: https://mlsanity.com/paper/2009.01573/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [An AutoML-based Approach to Multimodal Image Sentiment Analysis](https://mlsanity.com/paper/2102.08092/) (2021)
- [Towards Less Constrained Macro-Neural Architecture Search](https://mlsanity.com/paper/2203.05508/) (2022)
- [Are Neural Architecture Search Benchmarks Well Designed? A Deeper Look Into Operation Importance](https://mlsanity.com/paper/2303.16938/) (2023)
- [Guided Evolutionary Neural Architecture Search With Efficient Performance Estimation](https://mlsanity.com/paper/2208.06475/) (2022)
- [OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System](https://mlsanity.com/paper/2403.11536/) (2024)

## Abstract (arXiv)

The dominant approach for surface defect detection is the use of hand-crafted feature-based methods. However, this falls short when conditions vary that affect extracted images. So, in this paper, we sought to determine how well several state-of-the-art Convolutional Neural Networks perform in the task of surface defect detection. Moreover, we propose two methods: CNN-Fusion, that fuses the prediction of all the networks into a final one, and Auto-Classifier, which is a novel proposal that improves a Convolutional Neural Network by modifying its classification component using AutoML. We carried out experiments to evaluate the proposed methods in the task of surface defect detection using different datasets from DAGM2007. We show that the use of Convolutional Neural Networks achieves better results than traditional methods, and also, that Auto-Classifier out-performs all other methods, by achieving 100% accuracy and 100% AUC results throughout all the datasets.