CorrDetector: A Framework for Structural Corrosion Detection from Drone Images using Ensemble Deep Learning

• URL: http://arxiv.org/abs/2102.04686v1
• Date: Tue, 9 Feb 2021 07:27:16 GMT
• Title: CorrDetector: A Framework for Structural Corrosion Detection from Drone Images using Ensemble Deep Learning
• Authors: Abdur Rahim Mohammad Forkan, Yong-Bin Kang, Prem Prakash Jayaraman, Kewen Liao, Rohit Kaul, Graham Morgan, Rajiv Ranjan, Samir Sinha
• Abstract summary: We propose a new technique that applies automated image analysis in the area of structural corrosion monitoring. Our study demonstrates that the ensemble approach of model significantly outperforms the state-of-the-art in terms of classification accuracy.
• Score: 2.108696645677565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \model significantly outperforms the state-of-the-art in terms of classification accuracy.

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