JF-YOLO: the jellyfish bloom detector based on deep learning

The unmonitored jellyfish boom inevitably destroys coastal biodiversities as a type of planktons with extremely high fecundity. It even seriously endangers people’s economic and social activities, such as clogging the water intake system of hydropower plants and hindering coastal tourism development. In the past, underwater video monitoring tended to be time-consuming and costly. This paper proposes JF-YOLO: an automatic jellyfish blooms detection model based on deep learning. We collecte many jellyfish videos in real environments to form a dataset for model training. JF-YOLO uses the improved YOLO-V4 detection model to ensure detection accuracy and speed. The experimental results show that the detection accuracy of the JF-YOLO network is better than that of the YOLO-V4 network, with the average detection accuracy increasing from 85.35% to 92.67% and the recall rate increasing from 72.32% to 85.74%. As a promising solution, JF-YOLO can effectively monitor the number or density of jellyfish and provide early warning when they appear abnormal, bringing convenience to ocean governance.