  Flexible road pavements deteriorate primarily due to traffic and adverse
environmental conditions. Cracking is the most common deterioration mechanism;
the surveying thereof is typically conducted manually using internationally
defined classification standards. In South Africa, the use of high-definition
video images has been introduced, which allows for safer road surveying.
However, surveying is still a tedious manual process. Automation of the
detection of defects such as cracks would allow for faster analysis of road
networks and potentially reduce human bias and error. This study performs a
comparison of six state-of-the-art convolutional neural network models for the
purpose of crack detection. The models are pretrained on the ImageNet dataset,
and fine-tuned using a new real-world binary crack dataset consisting of 14000
samples. The effects of dataset augmentation are also investigated. Of the six
models trained, five achieved accuracy above 97%. The highest recorded accuracy
was 98%, achieved by the ResNet and VGG16 models. The dataset is available at
the following URL: https://zenodo.org/record/7795975
