摘要
为了应对海上开阔环境目标检测面临的挑战,并致力于开发先进的海上目标检测技术,文章提出了一种基于深度学习框架YOLOv5的海上船舶自动检测与识别技术。该技术具备实时检测海上船舶等小目标的能力,并且体积较小,克服了传统目标检测技术在速度和性能上的限制。这项技术的应用能够有效提高航海运输、海上搜救和国防安全等领域的效率和可靠性。实验结果表明,文章提出的针对船舶视觉识别的方案快速而精准,能够验证YOLOv5在船舶检测中的重要价值,为相关领域的应用提供了重要的支持和保障。
To address the challenges of target detection in the vast marine environment and develop advance marine target detection technology,this paper proposes a marine ship automatic detection and recognition technology based on the deep learning framework YOLOv5.The technology enables real-time detection of small targets such as marine ships,with a compact size that overcomes the speed and performance limitations of traditional target detection techniques.Its application can effectively enhance efficiency and reliability in marine transportation,marine search and rescue,and national defense security fields.The experimental results demonstrate that the proposed solution for ship visual recognition is both rapid and accurate,confirming the significant value of YOLOv5 in ship detection and providing crucial support and assurance for relevant applications.
作者
董松
王奕森
丁紫璇
DONG Song;WANG Yisen;DING Zixuan(School of Intelligent Equipment Engineering,Wuxi Taihu University,Wuxi 214064,China)
出处
《无线互联科技》
2024年第14期1-3,共3页
Wireless Internet Technology
基金
2023年江苏省大学生创新创业项目,项目编号:202313571003Z。
