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

• URL: http://arxiv.org/abs/2009.01573v1
• Date: Thu, 3 Sep 2020 10:39:02 GMT
• Title: Auto-Classifier: A Robust Defect Detector Based on an AutoML Head
• Authors: Vasco Lopes, Lu\'is A. Alexandre
• Abstract summary: We show that the use of Convolutional Neural Networks achieves better results than traditional methods. We also show that Auto-Classifier out-performs all other methods, by achieving 100% accuracy and 100% AUC results throughout all the datasets.
• Score: 2.685668802278155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

Err

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.