基于YOLO-IST的红外船舶目标检测算法研究  

Infrared Ship Target Detection Algorithm Based on YOLO-IST

作　　者：陈里里[1] 杨维川 张程旺 赵鑫 CHEN Lili;YANG Weichuan;ZHANG Chengwang;ZHAO Xin(School of Information Science and Engineering,Chongqing Jiaotong University,Chongqing 400074,China;School of Mechatronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China)

机构地区：[1]重庆交通大学信息与科学工程学院,重庆400074 [2]重庆交通大学机电与车辆工程学院,重庆400074

出　　处：《重庆交通大学学报(自然科学版)》2025年第3期79-87,共9页Journal of Chongqing Jiaotong University(Natural Science)

基　　金：重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0075);交通工程应用机器人重庆市工程实验室2020年度开放课题项目(CELTEAR-KFKT-202003);重庆市社会事业与民生保障科技创新专项项目(cstc2017shmsA30016)。

摘　　要：针对红外船舶图像目标特征模糊、背景复杂以及小目标漏检等问题,基于YOLOv8提出一种面向海上交通中船舶目标的检测算法YOLO-IST(YOLO for infrared ship target)。在基线模型的骨干网络中引入C2f_DBB模块和CPCA注意力机制,通过增加特征提取层来提升模型对目标的识别能力;利用C2f_Faster_EMA模块替换颈部网络中的C2f模块,以提升模型检测精度和速度;采用多重注意力的动态检测头Dynamic Head优化模型框架,增强模型对小船舶目标的检测效果。研究结果表明:YOLO-IST的召回率R_(ecall)、精确率P_(recision)、平均精度M_(ap@50)、平均精度M_(ap@50-95)和F_(1score)分别达到89.7%、90.5%、94.7%、66.6%、90.1%,较基线模型YOLOv8分别提升了4.5%、3.8%、4.4%、4.7%、4.2%。该模型的提出在海上智能交通中具有较广泛的应用前景。Aiming at the problems of blurred target features,complex background and missed detection of small targets in infrared ship images,a detection algorithm YOLO-IST(YOLO for infrared ship target) for ship targets in maritime traffic was proposed based on YOLOv8.Firstly,the C2f_DBB module and CPCA attention mechanism were introduced into the backbone network of the baseline model,and the recognition ability of the model to the target was improved by adding the feature extraction layer.Then,the C2f_Faster_EMA module was used to replace the C2f module in the neck network to improve the detection accuracy and speed of model.Finally,the multi-attention dynamic detection head,that is Dynamic Head,was used to optimize the model framework and enhance the detection effect of the model to small ship targets.The experimental results show that R_(ecall),P_(recision),M_(ap@50)、M_(ap@50-95) and F_(1score) of YOLO-IST are 89.7%,90.5%,94.7%,66.6% and 90.1%,respectively,which are improved by 4.5%,3.8%,4.4%,4.7% and 4.2%,respectively,compared to the baseline model YOLOv8.The proposed model has a wide application prospect in maritime intelligent transportation.

关 键 词：交通运输工程 船舶工程 红外目标检测 YOLOv8 注意力机制 

分 类 号：U665.25[交通运输工程—船舶及航道工程]