The goal of anomaly detection is to identify and localize anomalous regions in captured objects, a key element of ensuring quality in modern manufacturing processes. However, many existing approaches often fail to meet all the industry's requirements, which include consistency, fast operation, and high performance, as well as the ability to effectively utilize all the available training data. In this work, we present a novel discriminative method, SuperSimpleNet, which evolved from SimpleNet, that addresses these shortcomings. SuperSimpleNet enhances detection performance, inference speed, and training stability, while it's one of the very few methods that supports operation in unsupervised, weak, mixed, and fully supervised setting. This allows for the utilization of all available data. Moreover, the introduction of active learning aids with the smart selection of new samples for annotation, further optimizing the process. SuperSimpleNet surpasses all existing methods with AUC of 98.0 % on SensumSODF dataset and achieves competitive results with AP 97.8 % on KolektorSDD2, AUC 93.6 % on VisA, and AUC 98.3 % on MVTec AD. It is also one of the fastest methods, with inference time of 9.5 ms and throughput of 262 images per second.
|
