Image augmentation to improve construction resource detection using generative adversarial networks, cut-and-paste, and image transformation techniques

Abstract

The paper proposes an image augmentation method to construct a large-size dataset for improving construction resource detection. The method consists of three techniques: removing-and-inpainting, cut-and-paste, and image-variation. The removing-and-inpainting technique arbitrarily removes objects from images and reconstructs the removed regions via generative adversarial networks (GAN). The cut-and-paste technique extracts objects from the original dataset and places them into the reconstructed images via the previous technique. The image-variation technique applies three image transformation techniques, intensity-, blur- and scale-variation, to the images. To evaluate the method, 656 unmanned aerial vehicle (UAV)-acquired construction site images were used as the original dataset. A faster region-based convolutional neural network (Faster R-CNN) trained with the augmented training dataset achieves better performance, which is higher than that of a network trained with the original dataset. These results prove that the method is optimal for improving construction resource detection in UAV-acquired images.