A Lightweight Model for Real-Time Monitoring of Ships

Abstract

Real-time monitoring of ships is crucial for inland navigation management. Under complex conditions, it is difficult to balance accuracy, real-time performance, and practicality in ship detection and tracking. We propose a lightweight model, YOLOv8-FAS, to address this issue for real-time ship detection and tracking. First, FasterNet and the attention mechanism are integrated and introduced to achieve feature extraction simply and efficiently. Second, the lightweight GSConv convolution method and a one-shot aggregation module are introduced to construct an efficient network neck to enhance feature extraction and fusion. Furthermore, the loss function is improved based on ship characteristics to make the model more suitable for ship datasets. Finally, the advanced Bytetrack tracke is added to achieve the real-time detection and tracking of ship targets. Compared to the YOLOv8 model, YOLOv8-FAS reduces computational complexity by 0.8×109 terms of FLOPs and reduces model parameters by 20%, resulting in only 2.4×106 parameters. The mAP-0.5 is improved by 0.9%, reaching 98.50%, and the real-time object tracking precision of the model surpasses 88%. The YOLOv8-FAS model combines light weight with high precision, and can accurately perform ship detection and tracking tasks in real time. Moreover, it is suitable for deployment on hardware resource-limited devices such as unmanned surface ships.

Keywords

• ship monitoring
• deep learning
• lightweight model
• real-time tracking
• YOLOv8