EMTT-YOLO: An Efficient Multiple Target Detection and Tracking Method for Mariculture Network Based on Deep Learning

Abstract

Efficient multiple target tracking (MTT) is the key to achieving green, precision, and large-scale aquaculture, marine exploration, and marine farming. The traditional MTT methods based on Bayes estimation have some pending problems such as an unknown detection probability, random target newborn, complex data associations, and so on, which lead to an inefficient tracking performance. In this work, an efficient two-stage MTT method based on a YOLOv8 detector and SMC-PHD tracker, named EMTT-YOLO, is proposed to enhance the detection probability and then improve the tracking performance. Firstly, the first detection stage, the YOLOv8 model, which adopts several improved modules to improve the detection behaviors, is introduced to detect multiple targets and derive the extracted features such as the bounding box coordination, confidence, and detection probability. Secondly, the particles are built based on the previous detection results, and then the SMC-PHD filter, the second tracking stage, is proposed to track multiple targets. Thirdly, the lightweight data association Hungarian method is introduced to set up the data relevance to derive the trajectories of multiple targets. Moreover, comprehensive experiments are presented to verify the effectiveness of this two-stage tracking method of the EMTT-YOLO. Comparisons with other multiple target detection methods and tracking methods also demonstrate that the detection and tracking behaviors are improved greatly.