Deep learning algorithm as a strategy for detection an invasive species in uncontrolled environment

Knowledge and monitoring of invasive species are fundamental measures to determine the short- and long-term effect on invaded ecosystems, in addition to developing strategies to control the problem or its specific solution. In this context, the lionfish is an invasive species that worries managers and scientists of fisheries and marine conservation, this is due to the affected area that spread starting from the east coast of the United States to the coasts of Brazil and it is recently extending to include the Mediterranean Sea. The diet of the invasive fish is small species of fish, crustaceans and invertebrates; the consequent damage is the decrease of food for species at the next level of the food chain and the lack of species to keep coral reefs healthy. In this paper, we propose a lionfish detection system that will be installed in an autonomous underwater vehicle, as part of a monitoring strategy that will allow real-time determination of the number of Lionfish, their location and without human intervention. We compared two detection systems, namely YOLOv4 and SSD-Mobilenet-v2, by training with cross-validation and evaluation with the test set we obtained the best model with 63.66% recall, 89.79% precision, and 79.15% mAP with images in the natural environment, implemented on NVIDIA's Jetson Nano embedded system.