  Deep learning-based construction-site image analysis has recently made great
progress with regard to accuracy and speed, but it requires a large amount of
data. Acquiring sufficient amount of labeled construction-image data is a
prerequisite for deep learning-based construction-image recognition and
requires considerable time and effort. In this paper, we propose a "data
augmentation" scheme based on generative adversarial networks (GANs) for
construction-equipment classification. The proposed method combines a GAN and
additional "adversarial training" to stably perform "data augmentation" for
construction equipment. The "data augmentation" was verified via binary
classification experiments involving excavator images, and the average accuracy
improvement was 4.094%. In the experiment, three image sizes (32-32-3, 64-64-3,
and 128-128-3) and 120, 240, and 480 training samples were used to demonstrate
the robustness of the proposed method. These results demonstrated that the
proposed method can effectively and reliably generate construction-equipment
images and train deep learning-based classifiers for construction equipment.
