Data-driven methods for detection of abnormal ship behavior: Progress and trends

Maritime traffic safety influences the development of world economies. A major aspect to enhance maritime traffic safety is the effective detection of abnormal ship behavior (DASB) which recently have been widely made based on data-driven methods using multisource heterogeneous data. In order to provide an overview of the state-of-the-art of research, this study presents a review of DASB. First, the categories of abnormal ship behavior and data sources of DASB are concretely introduced, and the process of data-driven DASB combined with expert knowledge are described. Second, we conduct systematic disciplinary knowledge maps in the field of DASB, which identify evolution, hotspots and emerging trends. In this manner, data-driven methods for DASB were categorized into six types, including multisource data fusion approaches, statistical analysis approaches, traditional intelligent algorithmic approaches, deep learning approaches, knowledge-based and data-driven integrating approaches and computing power, and provide an overview of them. Then, we propose an integrated framework for DASB. Finally, we discuss the challenges in terms of three technical aspects (data, algorithm, and computing power) and outline possible paths of investigation for DASB to improve intelligent maritime surveillance. •Mapping knowledge domain of bibliometrics is proposed to systematically analyze the detection of abnormal ship behavior.•Six data-driven methods of abnormal ship behavior detection are captured.•The research themes are roughly grouped into three core paths of data, algorithm, and computing power.•There is an integrated framework for multisource data-driven detection of abnormal ship behavior.