An end-to-end framework for anomaly detection and categorization

Abstract

In this study, we proposed an end-to-end framework for anomaly detection, classification in Industry 4.0 using deep learning models YOLO V8 and ResNet on the MVTec Anomaly Detection(MVTec AD) dataset. The framework is based on defect detection, anomaly localization. The multitask queues in YOLO V8 guarantee both: fast and precise detection in real time, while ResNet primarily suited for classification, complete with top notch precision and recall metrics. The metrics used for evaluation (including AUC, accuracy, precision, recall, F1 score and AP) confirm the good performance of the models. We also provide decision surface visualizations through Grad-CAM and Integrated Gradients that will help you understand some of the decisions made by the model. The YOLO V8 performed optimal on real-time detection tasks and ResNet performed best on classification accuracy, as highlighted through the results. This framework allows for the automation of anomaly detection and the resolution through investigation, unlocking future opportunities for real time anomaly detection and management.