论文标题
强大的异常图通过监督分类技术辅助多重缺陷检测
Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques
论文作者
论文摘要
行业4.0旨在通过利用新的技术进步（例如新的传感能力和人工智能）来优化制造环境。 DRAEM技术已显示出无监督分类的最先进的性能。创建异常地图突出显示缺陷可能谎言的区域的能力可以利用谎言来为监督分类模型提供线索并提高其性能。我们的研究表明，当通过提供图像和相应的异常图作为输入来训练缺陷检测模型时，可以实现最佳性能。此外，当将缺陷检测构建为二进制或多类分类问题时，这种设置提供了一致的性能，并且不受类平衡策略的影响。我们在三个数据集上进行了实验，并提供了由飞利浦消费者生活方式BV提供的实际数据。
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.
