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

TL;DR

This paper introduces Auto-Classifier, an AutoML-based enhancement for CNNs that significantly improves surface defect detection accuracy, outperforming traditional and other CNN methods across multiple datasets.

Contribution

The paper proposes Auto-Classifier, a novel AutoML-driven modification to CNN classification components, achieving superior defect detection performance.

Findings

Auto-Classifier achieves 100% accuracy on all datasets.

CNN-based methods outperform traditional defect detection approaches.

Auto-Classifier outperforms other CNN fusion methods.

Abstract

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.