文献类型：期刊文献

英文题名：Maritime man-overboard search using a lightweight and efficient end-to-end detection transformer

作者：Xu, Guokang[1];Yin, Jianchuan[1,2];Wang, Nini[3];Zhang, Zeguo[1]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524009, Peoples R China;[2]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China;[3]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang 524009, Peoples R China

年份：2026

卷号：7

期号：2

外文期刊名：JOURNAL OF SAFETY SCIENCE AND RESILIENCE

收录：ESCI(收录号：WOS:001669045300001)、WOS

基金：This work was supported by the National Natural Science Foundation of China under Grants 52271361 and 52231014, the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008, the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684, and the Post-graduate Education Innovation Project of Guangdong Ocean University 202546.

语种：英文

外文关键词：Maritime safety; Small object detection; Maritime search; Man-overboard detection; ManOverboard

外文摘要：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 APsmall 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 seafarers' lives and ensuring the reliability of the world's essential trade routes.