论文标题
自动分类器：基于汽车头的健壮缺陷检测器
Auto-Classifier: A Robust Defect Detector Based on an AutoML Head
论文作者
论文摘要
表面缺陷检测的主要方法是使用基于手工特征的方法。但是，当影响提取的图像的条件变化时，这会落下。因此，在本文中，我们试图确定几个最先进的卷积神经网络在表面缺陷检测任务中的表现。此外，我们提出了两种方法：CNN融合，将所有网络的预测融合到最终网络和自动分类器中，这是一个新颖的建议，它通过使用AUTOML修改其分类组件来改善卷积神经网络。我们使用DAGM2007的不同数据集进行了实验，以评估表面缺陷检测任务中提出的方法。我们表明，使用卷积神经网络的使用比传统方法更好，并且通过在所有数据集中实现100％的准确性和100％的AUC结果，自动分类器会超越所有其他方法。
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.
