农业机械学报2025，Vol.56Issue(9)：667-676,10.DOI:10.6041/j.issn.1000-1298.2025.09.057
基于轻量化YOLO v8和BoT-SORT的石斑鱼跟踪方法
Grouper Fish Tracking Method Based on Lightweight YOLO v8 and BoT-SORT
摘要
Abstract
In aquaculture,fish tracking is fundamental for monitoring fish behavior,detecting water quality anomalies,and assessing the growth conditions of fish.However,existing methods suffer from issues such as computational time consumption,large model size,and difficulties in deploying on edge devices.To address these challenges,taking grouper fish as the research subject,a tracking method was proposed based on a lightweight YOLO v8 and BoT-SORT.This method consisted of two stages:target detection and target tracking.In the target detection phase,YOLO v8m was used as the baseline network.Firstly,a convolutional module,FasterConv,was introduced to reduce the number of parameters.Then the excitation and modulation attention(EMA)mechanism was incorporated to maintain model accuracy.Finally,a multi-scale feature fusion module,Fusion,was employed and the structure of the Neck network was adjusted to enhance the model's capability for feature integration.For the target tracking part,the BoT-SORT algorithm simplified the motion state variables of the fish body and included camera motion compensation(CMC)to deal with drastic changes in fish appearance.Subsequently,the ResNeST50 network was utilized to extract the appearance features of the fish within high-confidence detection bounding boxes,achieving fish tracking.The method was trained and validated on a self-built grouper dataset,achieving a mAP@0.5 of 95.80％for the object detection model,with a model size of 23.7 MB-a 54.42％reduction compared to that of the original YOLO v8m model.When applying the lightweight object detection model to the BoT-SORT algorithm,the MOTA reached 78.774％,with an FPS of 28.20 f/s,significantly outperforming SORT,DeepMoT,and other algorithms in comparative experiments.The results demonstrated that this method can achieve detection and tracking of grouper fish,providing technical support for the cultivation of groupers.关键词
石斑鱼/目标检测/目标跟踪/YOLO v8/BoT-SORT/轻量化Key words
grouper fish/object detection/object tracking/YOLO v8/BoT-SORT/lightweight分类
信息技术与安全科学引用本文复制引用
段青玲,乔雅琪,刘怡然,冯晓晓,冉逊,刘春红..基于轻量化YOLO v8和BoT-SORT的石斑鱼跟踪方法[J].农业机械学报,2025,56(9):667-676,10.基金项目
国家重点研发计划项目(2022YFD2001701)和重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-ZXX0053) （2022YFD2001701）
