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9 May 2024 Single image wire corrosion level identification using explainable AI
Shibin Parameswaran, Sara Wheeland, Ivan Febus-Rivera
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Proceedings Volume 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII; 129500U (2024) https://doi.org/10.1117/12.3027476
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States
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Abstract
Guy wires stabilize outdoor structures typically up to hundreds of feet in height. Exposed to potentially harsh environmental conditions, these wires can experience internal and external corrosion. Up-close visual inspection is often unrealistic given the height. A typical way to characterize corrosion involves taking samples of the wire, which requires destruction and replacement of the current wire. Non-destructive methods are preferred for this reason. To this end, we explore the feasibility of using computer vision techniques on images captured from Unmanned Aerial Vehicles (UAV) to automatically detect corrosion or swelling due to internal corrosion on a wire or cable. We leverage a data-centric approach to train a classifier for identifying corrosion level from a single RGB image. We evaluate the model performance on a dataset of wire images displaying 4 different corrosion levels. Finally, in order to provide additional insights and explainability to a human operator, we use GradCam to analyze the model's decision-making process and identify parts of an image that contributed to the resulting corrosion level label.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shibin Parameswaran, Sara Wheeland, and Ivan Febus-Rivera "Single image wire corrosion level identification using explainable AI", Proc. SPIE 12950, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII, 129500U (9 May 2024); https://doi.org/10.1117/12.3027476
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KEYWORDS
Corrosion
Image processing
Artificial intelligence
Deep learning
Computer vision technology
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