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.
|Journal||Automation in Construction|
|Publication status||Published - 2020 Jul|
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (No. 2018R1A2B2008600 ) and the Ministry of Education (No. 2018R1A6A1A08025348 ).
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction