基于边缘计算的海上养殖鱼群实时追踪系统

Real-time fish tracking system based on edge computing for marine farming

为解决实际养殖环境中存在鱼群数据集少、追踪算法效果不稳定、追踪部署成本高等问题,结合鱼群行为数据、鱼群追踪算法和边缘计算平台3方面建立一套基于边缘计算的鱼群实时追踪系统。采集并标注养殖环境中的金鲳鱼视频以建立鱼群行为数据集;提出基于检测的鱼群追踪算法FishTrack,改进追踪轨迹的关联策略,以减少因鱼群姿态变换和长期遮挡导致的目标丢失;将FishTrack部署在轻量级边缘计算设备上,保证追踪效果的同时降低了计算成本。研究结果表明:FishTrack在金鲳鱼数据集上的追踪准确率可达72.95%,比当前主流追踪算法ByteTrack的目标交换错误率降低了83%;实时追踪速度为平均7.40帧/s,适用于真实生产环境中鱼群的实时追踪。

Scarce fish dataset for aquaculture,an unstable tracking algorithm,and high costs all hinder the technique from being deployed.To solve these problems,a high-performance and real-time fish tracking system was built based on three aspects:fish behavior data,the fish tracking algorithm,and the edge computing platform.In terms of data,the pomfret tracking dataset was collected and labeled on pomfret activity video from the breeding environment.A detection-based fish tracking algorithm named FishTrack was proposed,which enhanced the object association strategy to reduce the tracking lost caused by pose change and long-time occlusion.The FishTrack was deployed on the lightweight edge platform enabling high performance with low computational costs.The results show that FishTrack achieves the tracking accuracy of 72.95%and reduces identification switch of ByteTrack by 83%.Moreover,the tracking system works with a running speed of 7.40 frame/s,which is suitable for real-time tracking under production environment.