Enhanced Small Ship Segmentation with Optimized ScatYOLOv8+CBAM on Embedded Systems

Abstract

To enhance maritime situational awareness, real-time segmentation of small or distant ships from optical monitoring footage, poses significant performance challenges, especially on embedded systems. Efficient processing of full-resolution images is essential for precise small ship segmentation. In this paper, we introduce a framework that combines an optimized version of ScatYOLOv8+CBAM with a custom batch-processed Slicing Aided Hyper Inference (SAHI). This approach is aimed at efficient and accurate small ship segmentation, deployed on embedded systems, and is validated using a real-world maritime dataset (ShipSG). With our optimized ScatYOLOv8+CBAM, we substantially improve inference efficiency with a 36% faster inference speed compared to its predecessor in the lightest model size, without compromising segmentation accuracy. Additionally, the integration of batch-processed SAHI, despite an increase in computation time, improves the accuracy of small ship segmentation up to 11%, allowing more effective utilization of full-resolution imagery without compromising the computational resources of embedded platforms. Our findings set a new benchmark for embedded maritime monitoring and pave the way for future research to optimize real-time high-resolution processing in resource-constrained environments.