Maritime man-overboard search using a lightweight and efficient end-to-end detection transformer

Maritime transportation plays a crucial role in global economic trade. However, maritime accidents occur frequently, posing significant threats to the safety of seafarers. In search and rescue scenarios for man-overboard, unmanned aerial vehicles (UAVs) are gradually replacing manned aircraft and helicopters. Besides, detecting man-overboard is challenging because of their small pixel size, weak signals, and indistinct features on the ocean surface. Furthermore, existing detectors struggle to strike a balance between lightweight design for UAVs and detection accuracy. To address this issue, the novel Man-overboard Detection Transformer (MOB-DETR) is proposed. On the one hand, the Token Enhancement layer is introduced, which conducts fine-grained filtering of spatial and channel dimensions, reducing redundant encoding caused by background queries. On the other hand, the Effusion Fusion Module, based on the RepViT Block, is proposed, effectively eliminating computational redundancy by decoupling the interaction mechanisms between spatial and channel dimensions. Additionally, to fill the existing gap in benchmark datasets for detecting man-overboard, the ManOverboard benchmark dataset has been established. In the experimental validation phase, MOB-DETR is conducted on ManOverboard and SeaDronesSeev2. Ablation experiments show that MOB-DETR achieves 11.7 % better lightweight performance and 14.4 % higher A P small than baselines. Comparison experiments on ManOverboard and SeaDronesSeev2 validate its effectiveness, offering an efficient solution for man-overboard detection. Overall, this research not only advances man-overboard detection but also significantly enhances the resilience of maritime transportation, ultimately protecting the lives of seafarers and ensuring the reliability of the world's essential trade routes.