MES-YOLO: An efficient lightweight maritime search and rescue object detection algorithm with improved feature fusion pyramid network

IF 2.6 4区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Visual Communication and Image Representation Pub Date : 2025-04-07 DOI:10.1016/j.jvcir.2025.104453

Zhao Jin , Tian He , Liping Qiao , Jiang Duan , Xinyu Shi , Bohan Yan , Chen Guo

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引用次数: 0

Abstract

Maritime Search and Rescue (SAR) object detection is challenged by environmental complexity, variability in object scales, and real-time computation constraints of Unmanned Aerial Vehicles (UAVs). Our MES-YOLO algorithm, designed for maritime UAV imagery, employs an innovative Multi Asymptotic Feature Pyramid Network (MAFPN) to enhance detection accuracy across scales. It integrates an Efficient Module (EMO) and Inverted Residual Mobile Blocks (iRMB) to maintain a lightweight model while enhancing key information perception.The SIoU loss function is used to optimize the detection performance of the model. Tests on the SeaDronesSee dataset show that MES-YOLO increased average precision (mAP50) from 81.5% to 87.1%, reduced parameter count by 43.3%, and improved the F1 score by 6.8%, with a model size only 58.3% that of YOLOv8, surpassing YOLO series and other mainstream algorithms in robustness to background illumination and imaging angles.

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MES-YOLO：一种基于改进特征融合金字塔网络的高效轻量级海上搜救目标检测算法

海上搜救（SAR）目标检测受到环境复杂性、目标尺度多变性和无人机实时性约束的挑战。我们的MES-YOLO算法专为海上无人机图像设计，采用创新的多渐近特征金字塔网络（MAFPN）来提高跨尺度的检测精度。它集成了高效模块（EMO）和反向残余移动块（iRMB），以保持轻量级模型，同时增强关键信息感知。利用SIoU损失函数优化模型的检测性能。在SeaDronesSee数据集上的测试表明，MES-YOLO将平均精度（mAP50）从81.5%提高到87.1%，参数数量减少43.3%，F1分数提高6.8%，模型尺寸仅为YOLOv8的58.3%，在对背景光照和成像角度的鲁棒性方面超过了YOLO系列等主流算法。

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来源期刊

Journal of Visual Communication and Image Representation 工程技术-计算机：软件工程

CiteScore

5.40

自引率

11.50%

发文量

188

审稿时长

9.9 months

期刊介绍： The Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems.