An Integrated Coating Inspection System for Marine and Offshore Corrosion Management

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) Pub Date : 2018-11-01 DOI:10.1109/ICARCV.2018.8581327

Lili Liu, E. Tan, Z. Cai, Yongda Zhen, Xieping Yin

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引用次数: 11

Abstract

The application of protective coatings is the primary method for protecting marine and offshore structures from coating breakdown and corrosion (CBC). The CBC assessment is a major aspect of coating failure management. Evaluation methods can result in unnecessary maintenance costs and a higher risk of failure. In order to improve efficiency and productivity, the micro-aerial vehicle (MAV) auxiliary automated CBC Evaluation System (A-CAS) is proposed for effective coating failure inspection. Compared to existing manual inspection solutions by surveyors, this method is more suitable for inspecting large areas by means of capturing and analyzing pictures/videos of the target areas. In this paper, a MAV facilitated CBC assessment system implementing deep learning for object recognition has been developed to provide effective CBC assessment for marine and offshore industries. By using active thermography, it is able to identify corrosion behind coatings. This will greatly improve the work efficiency and reliability of coating inspection for surveyors.

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用于船舶和近海腐蚀管理的综合涂层检测系统

保护涂层的应用是保护海洋和近海结构物免受涂层破坏和腐蚀(CBC)的主要方法。CBC评估是涂层失效管理的一个重要方面。评估方法可能导致不必要的维护成本和更高的故障风险。为了提高涂装失效检测的效率和生产率，提出了微型飞行器(MAV)辅助自动涂层失效检测系统(A-CAS)。与现有的测量员人工巡检方案相比，该方法更适合于通过对目标区域的图片/视频进行捕捉和分析来进行大面积的巡检。本文开发了一种MAV辅助CBC评估系统，实现了深度学习的目标识别，为船舶和海洋工业提供有效的CBC评估。通过使用主动热成像，它能够识别涂层背后的腐蚀。这将大大提高测量员涂层检测的工作效率和可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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