Anomaly analysis is of great interest to diverse fields, including data mining and machine learning, and plays a critical role in a wide range of applications, such as medical health, credit card fraud, and intrusion detection. Recently, a significant number of anomaly detection methods with a variety of types have been witnessed. This paper intends to provide a comprehensive overview of the existing work on anomaly detection, especially for the data with high dimensionalities and mixed types, where identifying anomalous patterns or behaviours is a nontrivial work. Specifically, we first present recent advances in anomaly detection, discussing the pros and cons of the detection methods. Then we conduct extensive experiments on public datasets to evaluate several typical and popular anomaly detection methods. The purpose of this paper is to offer a better understanding of the state‐of‐the‐art techniques of anomaly detection for practitioners. Finally, we conclude by providing some directions for future research.

Cracks are one of the most common categories of pavement distress that may potentially threaten road and highway safety. Thus, a reliable and efficient pixel-level method of crack detection is necessary for real-time measurement of the crack. However, many existing encoder-decoder architectures for crack detection are time-consuming because the part of decoder module always has lots of convolutional layers and feature channels that lead to performance that highly relies on computing resources, which is a handicap in scenarios with limited resources. In this study, we propose a simple and effective method to boost the algorithmic efficiency based on encoder-decoder architecture for crack detection. We develop a switch module, called SWM, to predict whether the image is positive or negative and then skip the decoder module to save computation time when it is negative. This method uses the encoder module as the fixed feature extractor and only needs to place a light-weight classifier head on the end of the encoder module to output the final class probability. We choose the classical UNet and DeepCrack as examples of the encoder-decoder architectures to show how SWM is integrated into the architectures to reduce computation complexity. Evaluations on the public CrackTree206 and AIMCrack datasets demonstrate that our method can significantly boost the efficiency of the encoder-decoder architectures in all tasks, while without affecting the performance. The SWM can also be easily embedded into other encoder-decoder architectures for further improvement. The source code is available at https://github.com/hanshenchen/crack-detection.

Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection. They can be used for conventional data analysis. However, it is not a trivial work to identify rare behaviors or patterns out from complicated data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners. Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets. Finally, several challenging issues and future research directions are discussed.