Smart Informatix Laboratory

Home

Evaluation of Convolutional Neural Networks for Deep Learning-based Corrosion Detection

Corrosion is a major defect in structural systems that has a significant economic impact and can pose safety risks if left untended. Currently, an inspector visually assesses the condition of a structure to identify corrosion. This approach is time-consuming, tedious, and subjective. Robotic systems, such as unmanned aerial vehicles, paired with computer vision algorithms have the potential to perform autonomous damage detection that can significantly decrease inspection time and lead to more frequent and objective inspections. This study evaluates the use of convolutional neural networks (CNNs) for corrosion detection. A CNN learns the appropriate classification features that in traditional algorithms were hand-engineered. Eliminating the need for dependence on prior knowledge and human effort in designing features is a major advantage of CNNs. This study presents different CNN-based approaches for corrosion assessment on metallic surfaces.The effect of different color spaces, sliding window sizes, and CNN architectures are discussed. To this end, the performance of two pretrained state-of-the-art CNN architectures as well as two proposed CNN architectures are evaluated, and it is shown that CNNs outperform state-of-the-art vision-based corrosion detection approaches that are developed based on texture and color analysis using a simple multilayered perceptron network (MLP). Furthermore, it is shown that one of the proposed CNNs significantly improves the computational time in contrast with state-of-the-art pretrained CNNs while maintaining comparable performance for corrosion detection.

Related Publication

Deegan J. Atha and Mohammad R. Jahanshahi, (2017), "Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection," Structural Health Monitoring, accepted.