SDYOLO-Tracker:一种高效的多鱼缺氧行为识别和跟踪方法

IF 8.9 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2025-05-01 Epub Date: 2025-02-19 DOI:10.1016/j.compag.2025.110079
Jiaxuan Yu , Guangxu Wang , Xin Li , Zhuangzhuang Du , Wenkai Xu , Muhammad Akhter , Daoliang Li
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引用次数: 0

摘要

对养殖中鱼类进行实时监测和跟踪,及时发现鱼类缺氧行为,对推进智能养殖具有至关重要的作用。本研究提出了一种有效的多鱼行为识别与跟踪方法(SDYOLO-Tracker),以解决不同鱼种缺氧行为监测的滞后问题。我们的工作旨在实现对鱼类异常缺氧行为的早期监测。为了增强模型对小目标的检测能力,我们使用SPD-Conv和D-LKA模块对YOLOv8n模型进行了改进。然后,我们将改进的YOLOv8n (SDYOLOv8)模型与Bytetrack多目标跟踪(MOT)算法相结合。该方法利用互补的3D运动预测策略,有效地解决了鱼的突然运动和消失等问题,从而准确地识别和跟踪多鱼。实验结果表明,改进后的MOTA、HOTA、IDR和IDF1分别比原YOLOv8n模型提高了3.32%、3.53%、6.68%和5.36%。此外,在测试视频中,ID号切换减少了18.75%,在不影响模型速度的情况下显著提高了多鱼跟踪的准确性。在与其他MOT算法的对比实验中,该方法获得了最高的IDR和IDF1指标,帧/秒(FPS)为45.03,显示出最佳的跟踪稳定性和最快的模型处理速度。此外,SDYOLO-Tracker还可以对不同溶解氧浓度下的鱼类行为进行定性和定量分析,反映鱼类运动、平均速度和最大瞬时速度的变化。这些运动指标可以确定不同鱼类的缺氧阈值,为鱼类缺氧指标的研究提供了新的途径。综上所述,本研究对鱼类早期缺氧行为的研究具有一定的理论和现实意义。
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SDYOLO-Tracker: An efficient multi-fish hypoxic behavior recognition and tracking method
Real-time monitoring and tracking of fish in aquaculture and timely detection of fish hypoxia behavior play a crucial role in advancing the intelligent aquaculture. This study proposes an effective multi-fish behavior identification and tracking method (SDYOLO-Tracker) to solve the lag problem of hypoxia behavior monitoring in different species of fish. Our work aims to enable early monitoring of abnormal hypoxic behavior in fish. To enhance the model ability to detect small objects, we improved the YOLOv8n model using SPD-Conv and D-LKA modules. Then, we integrate the improved YOLOv8n (SDYOLOv8) model with Bytetrack multi-object tracking (MOT) algorithm. This approach utilizes a complementary 3D motion prediction strategy, which effectively addresses issues such as sudden fish motion and disappearance, resulting in an accurate multi-fish identification and tracking. The experimental results indicate that the enhanced MOTA, HOTA, IDR and IDF1 have shown improvement of 3.32 %, 3.53 %, 6.68 %, and 5.36 %, respectively when compared with the original YOLOv8n model. Moreover, in the tested video, the ID number switching is reduced by 18.75 %, significantly improving the accuracy of multi-fish tracking without compromising the speed of the model. In the comparative experiments with other MOT algorithms, this method achieved the highest IDR and IDF1 metrics, with a frame per second (FPS) of 45.03, demonstrating the best tracking stability and the fastest model processing speed. In addition, SDYOLO-Tracker can conduct qualitative and quantitative analyses of fish behavior under varying dissolved oxygen concentrations, reflecting changes in locomotion, average velocity, and maximum instantaneous velocity. These movement indexes allow for the determination of hypoxia thresholds of different fish species providing a novel approach for studying hypoxia indicators in fish. In conclusion, this research holds both theoretical and practical significance for the study of early hypoxia behavior in fish.
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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