Abstract
Fish Aggregating Devices (FADs) are floating objects used by fishers to facilitate their catches. The majority of the global industrial tropical tuna purse-seine catches currently occurs at FADs. One of the main adverse ecological impacts of FAD fisheries is the bycatch of vulnerable species such as pelagic sharks. Detecting the presence of sharks at FADs remotely, constitutes a key step to reduce their catches.
In this paper, we explain how an image-based shark detection module can be embedded on an autonomous buoy associated to a FAD. We discuss the general design, the hardware and software selection, the implementation of the detection algorithm based on Deep-Learning techniques and we detail the implementation inside the buoy. Some experiments allowed us to discuss energy consumption. This system could be integrated with other new technologies to mitigate bycatch in industrial tropical tuna purse-seine fisheries.