Anomaly detection is a challenging task and usually formulated as an unsupervised learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature alignment enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on three major benchmarks, significantly superior to the state of the art ones. In addition, it makes inferences at a very high speed (with 100 FPS for images of the size at 256×256), at least dozens of times faster than the latest counterparts.
License plate recognition is a core module for intelligent transportation systems, while license plate location is an important part of it. Haar-like cascade classifier is good for face detection, but its application to license plate localization largely depends on selection of positive and negative samples. In this paper we studied on how to choose good samples for Haar-like cascade classifiers and image postprocessing methods to achieve good location results. It is hoped that the study could be useful to guide sample preparation for other object detection using Haar-like cascade classifiers.
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