一种基于YOLOv11改进的水下船体清洗机器人目标检测方法

doi:10.19423/j.cnki.31-1561/u.2025.041

船舶 ›› 2025, Vol. 36 ›› Issue (04): 13-26.DOI: 10.19423/j.cnki.31-1561/u.2025.041

一种基于YOLOv11改进的水下船体清洗机器人目标检测方法

洪欣扬, 贾博伟, 陈道毅^*

清华大学深圳国际研究生院深圳 518055

收稿日期:2025-02-27 修回日期:2025-03-30 出版日期:2025-08-25 发布日期:2025-09-04
通讯作者: 陈道毅（1959-）,男,博士,教授/博士生导师。研究方向：海洋动力学,海洋仪器和装备,海洋卫星遥感和信息等。
作者简介:洪欣扬（1999-）,男,硕士研究生。研究方向：船舶清洗机器人感知系统。贾博伟（1999-）,男,硕士研究生。研究方向：船舶清洗机器人控制系统。陈道毅,教授,博士生导师,清华大学博士,美国康奈尔大学博士后。其历任英国曼彻斯特大学讲师和高级讲师,英国利物浦大学海事工程讲席教授;现任“深圳深海海洋工程装备配套试验平台”、“深圳市南海可燃冰资源开发公共技术服务平台”、“深圳市近海动力环境演变实验室”、“清华大学海洋技术研究中心”等科研平台主任;曾任清华大学深圳国际研究生院海洋工程研究院副院长、海洋学部主任。陈道毅教授是清华大学海洋学科的主要创始人,长期致力于海洋工程动力学、海洋能源和环境方面研究（主要包括：海洋环境与可燃冰、海洋仪器与装备、海洋流体与浅水动力学、海洋卫星遥感与信息等）;建设了先进的“工程水池-海上试验场-超算”三位一体海洋工程研发平台,在海洋环境监测技术、水合物（可燃冰）开采与利用关键技术、海上风电与深水油气工程安装技术、海洋环保技术等方面取得丰硕成果,在浅水动力学等方面更是取得了里程碑式的成就。其主持或以合作单位负责人身份承担国家重点研发计划、国家863计划、国家973计划等多个科研项目,累计发表学术论文100余篇,单篇论文被 SCI 引用达678次。

An Improved YOLOv11-Based Target Detection Method for Underwater Hull-Cleaning Robots

HONG Xinyang, JIA Bowei, CHEN Daoyi^*

Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

Received:2025-02-27 Revised:2025-03-30 Online:2025-08-25 Published:2025-09-04

摘要/Abstract

摘要： 在水下船体清洗任务中,对污损及障碍物等目标的检测和精准识别是提升自动化清洗效能的关键。当前水下船体清洗机器人多依赖人工视觉实现环境感知与目标定位,存在效率瓶颈和安全隐患。该文基于YOLOv11框架,提出改进模型 YOLO-HC,构建多尺度扩张注意力模块（MSDA_C2PSA）以增强跨尺度特征提取能力,采用双向加权特征金字塔优化多层级目标表征融合,并通过动态检测头集成多种注意力机制进一步提升检测鲁棒性。自建水下船体表面数据集实验表明：该模型在mAP@0.5和mAP@0.5:0.95指标上分别达到87.0%和62.4%,较基线模型分别提升2.2%和3.4%。该研究为水下船体清洗任务提供了准确高效的目标检测方法,推动了船体清洗向智能无人化目标更近一步。

关键词: 水下船体清洗, 目标检测, 清洗机器人, YOLOv11, 深度学习

Abstract: In underwater hull cleaning tasks, the detection and precise identification of fouling and obstacles by target detection technology are critical for enhancing the efficiency of automated cleaning. The current underwater-hull cleaning robots primarily rely on manual visual inspection for environmental perception and target localization, which imposes efficiency bottlenecks and safety hazards. An improved model, YOLO-HC, is proposed based on the YOLOv11 framework. The model enhances cross-scale feature extraction capability by constructing a Multi-scale Dilated Attention module (MSDA_C2PSA), optimizes multi-level target representation fusion by using a Bi-directional Weighted Feature Pyramid, and further improves detection robustness by integrating multiple attention mechanisms into the dynamic detection head. In experiments on a self-built underwater hull surface dataset, the model achieved 87.0% and 62.4% on the mAP@0.5 and mAP@0.5:0.95 metrics, with increases of 2.2% and 3.4% compared to the baseline model, respectively. It provides an accurate and efficient target detection method for underwater hull cleaning tasks, advancing the goal of intelligent and unmanned hull cleaning.

Key words: underwater hull-cleaning, target detection, cleaning robot, YOLOv11, deep learning

中图分类号:

洪欣扬, 贾博伟, 陈道毅. 一种基于YOLOv11改进的水下船体清洗机器人目标检测方法[J]. 船舶, 2025, 36(04): 13-26.

HONG Xinyang, JIA Bowei, CHEN Daoyi. An Improved YOLOv11-Based Target Detection Method for Underwater Hull-Cleaning Robots[J]. Ship & Boat, 2025, 36(04): 13-26.

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一种基于YOLOv11改进的水下船体清洗机器人目标检测方法

An Improved YOLOv11-Based Target Detection Method for Underwater Hull-Cleaning Robots

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