CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning
CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning
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DOI：
10.1016/j.eswa.2021.116461
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发表时间：
2022-01-14
影响因子：
8.5
通讯作者：
Sinha, Samir
中科院分区：
文献类型：
--
作者：
Forkan, Abdur Rahim Mohammad;Kang, Yong-Bin;Sinha, Samir
在本文中，我们提出了一种将自动图像分析应用于结构腐蚀监测领域的新技术，与现有方法相比，该技术的有效性得到了提高。结构腐蚀监测是基于风险的维护理念的第一步，它取决于工程师对建筑失效风险与维护财政成本之间的平衡评估。这引入了人为错误的机会，当限制使用无人机捕获的图像对由于许多背景噪声而无法到达的区域进行评估时，这将进一步复杂化。这个问题的重要性促进了一个活跃的研究社区，旨在通过使用人工智能（AI）图像分析来支持工程师进行腐蚀检测。在本文中，我们通过开发一个框架cordetector来推进这一领域的研究。cordetector采用基于卷积神经网络（cnn）的新型集成深度学习方法进行结构识别和腐蚀特征提取。我们使用无人机捕获的复杂结构（例如电信塔）的真实世界图像提供了经验评估，这是工程师的典型场景。我们的研究表明，CorrDetector的集成方法在分类精度方面明显优于最先进的方法。
In this paper, we propose a new technique that applies automated image analysis in the area of structural corrosion monitoring and demonstrate improved efficacy compared to existing approaches. Structural corrosion monitoring is the initial step of the risk-based maintenance philosophy and depends on an engineer's assessment regarding the risk of building failure balanced against the fiscal cost of maintenance. This introduces the opportunity for human error which is further complicated when restricted to assessment using drone captured images for those areas not reachable by humans due to many background noises. The importance of this problem has promoted an active research community aiming to support the engineer through the use of artificial intelligence (AI) image analysis for corrosion detection. In this paper, we advance this area of research with the development of a framework, CorrDetector. CorrDetector uses a novel ensemble deep learning approach underpinned by convolutional neural networks (CNNs) for structural identification and corrosion feature extraction. We provide an empirical evaluation using real-world images of a complicated structure (e.g. telecommunication tower) captured by drones, a typical scenario for engineers. Our study demonstrates that the ensemble approach of CorrDetector significantly outperforms the state-of-the-art in terms of classification accuracy.