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.

除了制定控制问题或其具体解决方案的策略之外，对入侵物种的了解和监测是确定对入侵生态系统的短期和长期影响的基本措施。在这种情况下，狮子鱼是一种入侵物种，使渔业和海洋保护的管理者和科学家感到担忧，这是由于受影响的区域从美国东海岸开始蔓延到巴西海岸，并且最近扩展到包括地中海。入侵鱼类的食物是小型鱼类、甲壳类和无脊椎动物；随之而来的损害是食物链下一级物种的食物减少，以及缺乏保持珊瑚礁健康的物种。在本文中，我们提出了一种狮子鱼检测系统，该系统将安装在自主水下航行器中，作为监控策略的一部分，该系统将允许实时确定狮子鱼的数量及其位置，而无需人工干预。我们比较了两个检测系统，即 YOLOv4 和 SSD-Mobilenet-v2，通过交叉验证和评估与测试集的训练，我们获得了具有 63.66% 召回率、89.79% 精度和 79.15% mAP 的最佳模型，具有自然图像环境，在 NVIDIA 的 Jetson Nano 嵌入式系统上实现。