Real-time, automatic total catch monitoring based on deep learning towards an efficient fishing activity management

Abstract

The successful implementation of the Common Fisheries Policy (CFP) depends, at a large extent, on the capacity to quantify total catches on board commercial vessels Because of the large number of fishing vessels and the high number of trips to be monitored, classic monitoring methods mainly based on inspections, are not effective --> The use of electronic devices to quantify fishing catches is gaining relevance The data provided by such devices, in combination with mathematical models, may be used to assess the state of the different fishing stocks and to optimize the fishing activity Increasingly though, technology has quickly developed during the last years to provide vision and artificial intelligence based remote Electronic Monitoring (REM or EM systems) at lower costs, and with more potential to cover large areas than traditional monitoring strategies