当前位置:
X-MOL 学术
›
Autom. Constr.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Automated detection of underwater dam damage using remotely operated vehicles and deep learning technologies
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-13 , DOI: 10.1016/j.autcon.2025.105971
Fei Kang, Ben Huang, Gang Wan
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-13 , DOI: 10.1016/j.autcon.2025.105971
Fei Kang, Ben Huang, Gang Wan
Underwater damage poses significant risks to the safe operation of dams, making timely detection critical. Traditional manual inspection methods are hazardous, time-consuming, and labor-intensive. This paper introduces an automated detection system integrating remotely operated vehicles (ROVs) and enhanced deep-learning technologies. The proposed YOLOv8n-DCW model incorporates deformable convolution networks, coordinate attention mechanisms (CoordAtt), and an improved loss function to boost detection performance. Trained on an underwater dam damage dataset, the model achieved an 84.5 % mean average precision. Ablation studies validated the effectiveness of these enhancements, while comparative experiments demonstrated the superiority of YOLOv8n-DCW over existing models and CoordAtt's advantage among attention mechanisms. The developed detection software, integrated with the ROV, was tested in a laboratory pool, confirming its practicality and efficiency. This system offers a safer, faster, and cost-effective solution for underwater dam damage detection, addressing limitations of traditional methods and providing a robust tool for engineering applications.
中文翻译:
使用遥控车辆和深度学习技术自动检测水下大坝损坏
水下损坏对大坝的安全运行构成重大风险,因此及时检测至关重要。传统的人工检测方法危险、耗时且劳动强度大。本文介绍了一种集成遥控潜水器 (ROV) 和增强深度学习技术的自动检测系统。所提出的 YOLOv8n-DCW 模型结合了可变形卷积网络、坐标注意力机制 (CoordAtt) 和改进的损失函数,以提高检测性能。在水下大坝损伤数据集上进行训练,该模型实现了 84.5% 的平均精度。消融研究验证了这些增强的有效性,而比较实验证明了 YOLOv8n-DCW 优于现有模型以及 CoordAtt 在注意力机制中的优势。开发的检测软件与 ROV 集成,在实验室池中进行了测试,证实了其实用性和效率。该系统为水下大坝损坏检测提供了一种更安全、更快速、更具成本效益的解决方案,解决了传统方法的局限性,并为工程应用提供了强大的工具。
更新日期:2025-01-13
中文翻译:

使用遥控车辆和深度学习技术自动检测水下大坝损坏
水下损坏对大坝的安全运行构成重大风险,因此及时检测至关重要。传统的人工检测方法危险、耗时且劳动强度大。本文介绍了一种集成遥控潜水器 (ROV) 和增强深度学习技术的自动检测系统。所提出的 YOLOv8n-DCW 模型结合了可变形卷积网络、坐标注意力机制 (CoordAtt) 和改进的损失函数,以提高检测性能。在水下大坝损伤数据集上进行训练,该模型实现了 84.5% 的平均精度。消融研究验证了这些增强的有效性,而比较实验证明了 YOLOv8n-DCW 优于现有模型以及 CoordAtt 在注意力机制中的优势。开发的检测软件与 ROV 集成,在实验室池中进行了测试,证实了其实用性和效率。该系统为水下大坝损坏检测提供了一种更安全、更快速、更具成本效益的解决方案,解决了传统方法的局限性,并为工程应用提供了强大的工具。