Detection, Tracking and Enumeration of Marine Benthic Organisms Using an Improved YOLO+DeepSORT Network

Abstract

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% [email protected] and 53.3% [email protected]: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.

Keywords

• Benthic organisms
• YOLOv5
• DeepSORT
• global context block
• NWD loss function