Detection, Tracking and Enumeration of Marine Benthic Organisms Using an Improved YOLO+DeepSORT Network
Liu J. Li Q. Song S. Kulyash K.
2024Institute of Electrical and Electronics Engineers Inc.
IEEE Access
2024#12113867 - 113877 pp.
For marine ranching, efficiently and accurately detecting, tracking, and enumeration of benthic organisms can help farmers understand the growth and population changes of marine products, avoid high-risk tasks, and analyze changes in the marine ecological environment. To address the problems of target occlusion, low detection accuracy, and numerous small targets in existing marine organism detection models in complex seabed environments, an improved YOLOv5+DeepSORT algorithm for detecting and tracking benthic organisms is proposed. This algorithm integrates the Global Context Block attention mechanism with the BottleneckCSP module to form a new BottleneckCSPGC module, enhancing feature extraction capabilities. Replace the original loss function with the Normalized Wasserstein Distance (NWD) loss function to improve the detection accuracy of small targets. Finally, experimental results show that the accuracy on the underwater dataset reached 87.1% mAP@0.5 and 53.3% mAP@0.5:0.95, which are 1.8% and 4.0% higher than YOLOv5, respectively. The use of DeepSORT for tracking and counting provides technical support for marine ranching supervision.
Benthic organisms , DeepSORT , global context block , NWD loss function , YOLOv5
Text of the article Перейти на текст статьи
Institute of Automation, Qilu University of Technology, Shandong Academy of Sciences, Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology, Jinan, 250014, China
Al-Farabi Kazakh National University, Department of Mathematics, Almaty, 050040, Kazakhstan
Institute of Automation
Al-Farabi Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026