文献类型：期刊文献

英文题名：A Survey of Small Sea-Surface Target Detection for Maritime Search and Rescue

作者：Yin, Jianchuan[1,2,3];Xu, Guokang[1,2];Wang, Ning[4];Wang, Nini[5];Zhang, Zeguo[1,2]

机构：[1]Guangdong Ocean Univ, Naval Architecture & Shipping Coll, Zhanjiang 524029, Peoples R China;[2]Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524002, Peoples R China;[3]Hainan Trop Ocean Univ, Int Nav Coll, Sanya 572022, Peoples R China;[4]Dalian Maritime Univ, Sch Marine Engn, Dalian 116026, Peoples R China;[5]Guangdong Ocean Univ, Coll Math & Comp, Zhanjiang 524088, Peoples R China

年份：2025

卷号：27

期号：1

起止页码：211

外文期刊名：IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:001638301900001)、、EI(收录号：20255119720193)、Scopus(收录号：2-s2.0-105024722729)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 52231014, Grant 52271361, Grant U23A20680, and Grant 52271306; in part by the Special Projects of Key Areas for Colleges and Universities in Guangdong Province under Grant 2021ZDZX1008; in part by the Natural Science Foundation of Guangdong Province of China under Grant 2023A1515010684; in part by the Program for Scientific Research Start-Up Funds of Guangdong Ocean University under Grant 060302132105; in part by the National Plan for the Special Support for Top-notch Talents of China National Plan for the Special Support for Top-notch Talents of China under Grant SQ2022QB00329; in part by Liaoning Leading Talent Project under Grant XLYC2202005; in part by the Liaoning Natural Science Foundation Youth Project A Category under Grant 2025-JQ-01; in part by the Major Basic Research Project of Dalian Science and Technology Innovation Fund under Grant 2023JJ11CG009; and in part by the Fundamental Research Funds for the Central Universities under Grant 3132023501.

语种：英文

外文关键词：Object detection; Autonomous aerial vehicles; Sea surface; Marine vehicles; Reviews; Feature extraction; Accuracy; Benchmark testing; Systematics; Surveys; Maritime search and rescue; small sea-surface target; target detection; deep learning

外文摘要：The detection of small surface targets plays a critical role in maritime search and rescue (SAR) operations, ensuring the safety of people and property at sea. This paper provides a comprehensive review of the latest advancements and research in small sea surface target detection for maritime SAR missions. Deep learning-based models facilitate accurate target detection and localization by transforming image or video frames into high-dimensional abstract representations, enabling effective detection in complex sea surface environments. However, challenges such as occlusion, blurring, and reflections on the sea surface significantly complicate small target detection. To address these challenges, this paper summarizes a range of effective approaches, including context information, multi-scale learning, anchor-free detection, super-resolution, attention mechanisms, and sample-oriented approaches. These approaches aim to enhance the performance of small target detection in applications such as uncrewed aerial vehicles (UAV) and uncrewed supply vessels. Furthermore, this paper classifies small target datasets, providing a detailed overview based on their collection methods and application scenarios, while highlighting representative datasets. Through a thorough analysis of both methodologies and datasets, this paper offers valuable insights and directions for the future development of small target detection technology in maritime search and rescue operations.