Towards automatic anomaly detection in fisheries using electronic monitoring and automatic identification system

• Published in: Fisheries research Vol. 272; p. 106939
• Main Authors: Acharya, Debaditya, Farazi, Moshiur, Rolland, Vivien, Petersson, Lars, Rosebrock, Uwe, Smith, Daniel, Ford, Jessica, Wang, Dadong, Tuck, Geoffrey N., Little, L. Richard, Wilcox, Chris
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024
• Subjects: Anomaly detection; Automatic Identification System (AIS); cameras; Deep learning; Electronic Monitoring (EM); fisheries; Sustainable fisheries; Deep learning; Automatic Identification System (AIS); Electronic Monitoring (EM); Anomaly detection; Sustainable fisheries
• ISSN: 0165-7836
• DOI: 10.1016/j.fishres.2024.106939

To ensure sustainable fisheries, many complex on-vessel activities are periodically monitored to provide data to assist the assessment of stock status and ensure fishery regulations are being met. Such monitoring is often performed manually which is an exhaustive and expensive process. Consequently, several forms of Electronic Monitoring (EM) have emerged recently and include the use of electronic monitoring using on-board video cameras and Automatic Identification System (AIS). Unfortunately, insufficient cameras, ineffective camera position or obstructions, may lead to objects or behaviours of interest not being observed. In addition, more subtle, anomalous behaviours characteristic of behaviours of interest may still be captured. With the increasing success of deep learning methods, this article identifies the scope and challenges of using state-of-the-art deep learning approaches to anomaly detection in fisheries, and in particular to automatically detect abnormal behaviours from on-board video cameras and AIS data in line with current fishing practices and regulations. This study will take us one step closer towards automatic anomaly detection frameworks that can potentially replace existing manual monitoring methods. •We review the scope and challenges of current Artificial Intelligence approaches to anomaly detection in fisheries.•A taxonomy of possible anomalies due to unexpected behaviors by fishers or fishing vessels is presented.•Recent approaches to identify anomalies focusing on in-vessel activities and fishing vessel behaviors is described.•The key challenges to apply deep learning methods for automated anomaly detection are identified.•Suitable measures to mitigate those challenges, and possible future directions are proposed.