  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.
