Novel computer vision tools applied to marine recreational fisheries spatial planning

Abstract

Successful marine spatial planning relies on understanding patterns of human use, with accurate, detailed, and up-to-date information about the spatial distribution of fishing effort. In commercial vessels, tracking systems like the vessel monitoring system (VMS) or the automatic identification system (AIS) have helped to maintain and enhance the biodiversity of areas by generating large sources of positional data that served for commercial marine spatial planning. However, there is no regulation regarding location systems such as VMS or AIS for marine recreational fishing boats. Obtaining spatial data on marine recreational fishing can be difficult and time-intensive given the widespread and variable nature of the fleet. Remote cameras and computer vision systems are increasingly used to overcome the cost limitations of these conventional methods. Here we show a novel high-resolution and low-cost tracking system based on photo time-lapses and state-of-the-art computer vision algorithms, including deep learning, to automatically classify and obtain precise trajectories of fishing and cruising boats in coastal areas. Our method contributes to the automatic surveillance of marine protected areas by providing an image-based tool for automatic, real-time monitoring. Our method also allows for determining the intensity and spatial-temporal distribution of recreational fishing effort, important to defining the sustainability of the activity and coastal areas. We finally discuss the opportunities and limitations of computer vision tools applied to marine recreational fisheries spatial planning.