POSEIDON-SAT: Data Enhancement for Optical Fishing Vessel Detection From Low-Cost Satellites

This paper presents POSEIDON-SAT, a novel dataset augmentation method designed to enhance the detection of fishing vessels using optical remote sensing technologies. Illegal fishing poses a significant threat to conservation and economic fishing zones, and its detection is often hindered by tactics such as the disabling or manipulation of Automatic Identification System (AIS) transponders. While convolutional neural networks (CNNs) have shown promise in ship detection from optical imagery, the fine-grained classification of fishing vessels is limited by the scarcity of detailed datasets, as these vessels are often underrepresented in existing databases. POSEIDON-SAT addresses this gap by augmenting datasets with synthesized fishing vessel instances, improving the performance of ship detection models, particularly in low-resource scenarios. This approach is tailored for use on low-power, edge computing platforms aboard small satellites, such as CubeSats, where computational resources are highly constrained. By comparing POSEIDON-SAT to traditional class-weighting techniques, we evaluate its impact on lightweight YOLO models optimized for real-time detection aboard such satellites. Our experimental results demonstrate that POSEIDON-SAT significantly improves detection accuracy while reducing false positives, making it an effective tool for enhancing the capabilities of remote sensing platforms in monitoring illegal fishing. This method holds promise for addressing the global challenge of illegal fishing through scalable, efficient satellite-based monitoring systems.