Influence of image noise on crack detection performance of deep convolutional neural networks
This addresses the problem of robust crack detection in noisy images for structural health monitoring, but it is incremental as it applies existing methods to a new data scenario.
The paper investigated how image noise affects the accuracy of deep convolutional neural networks for crack detection, finding that noise has a moderate to high impact on classification performance despite pre-processing, and developed an index to select AlexNet as the most efficient model based on computation timing and accuracy.
Development of deep learning techniques to analyse image data is an expansive and emerging field. The benefits of tracking, identifying, measuring, and sorting features of interest from image data has endless applications for saving cost, time, and improving safety. Much research has been conducted on classifying cracks from image data using deep convolutional neural networks; however, minimal research has been conducted to study the efficacy of network performance when noisy images are used. This paper will address the problem and is dedicated to investigating the influence of image noise on network accuracy. The methods used incorporate a benchmark image data set, which is purposely deteriorated with two types of noise, followed by treatment with image enhancement pre-processing techniques. These images, including their native counterparts, are then used to train and validate two different networks to study the differences in accuracy and performance. Results from this research reveal that noisy images have a moderate to high impact on the network's capability to accurately classify images despite the application of image pre-processing. A new index has been developed for finding the most efficient method for classification in terms of computation timing and accuracy. Consequently, AlexNet was selected as the most efficient model based on the proposed index.