Corrosion Detection using Deep Convolutional Neural Networks

Abstract

Regular inspection of civil infrastructure and mechanical systems is crucial for safe operations. Corrosion is an important type of deterioration in structural systems that can lead to catastrophic effects if untended. Manual inspection is currently the predominant method of inspection that is time-consuming, costly, tedious, and subjective. A less time consuming and inexpensive alternative to current corrosion monitoring methods is the use of optical instrumentation (e.g. digital cameras). Due to the recent advances in using Convolutional Neural Networks (CNNs), the vision-based classification performance of computers has been improved significantly. This study evaluates the use of a CNN for corrosion detection. The effect of different sliding window sizes used for classification is evaluated. The experimental results show that the performance of the CNN outperforms state-of-the-art vision-based corrosion detection algorithms