Road damage detection with bounding box and generative adversarial networks based augmentation methods

In this paper, based on the data augmentation techniques of bounding box augmentation and the road damage generative adversarial network based augmentation, a robust road damage detection method has been presented. To this end, first, Iran road damage dataset has been collected by means of a dashboard-installed mobile phone. After processing these images by the blind referenceless image spatial quality evaluator technique, the substandard and inferior data have been automatically eliminated. In the second step, based on the YOLOv5 with several different baseline models, an algorithm has been developed for detecting the road surface damages. In the third step, by using the traditional as well as the bounding box augmentation and road damage generative adversarial network based augmentation techniques, the precision and the robustness of road damage detector under different environmental and field conditions have been improved. Finally, through the ensemble of the best models, the final detector accuracy has been enhanced. The findings of this article indicate that by using the proposed approach, the values of mAP and F1-score are improved by 13.79% and 7.58%, respectively. The dataset and parts of the code are available at: . In this research, based on the data augmentation techniques of bounding box augmentation and the road damage generative adversarial network based augmentation, a robust road damage detection method has been presented. To this end, first, Iran road damage dataset has been collected .The findings of this paper indicate that by using the proposed approach, the values of mAP and F1-score are improved by 13.79% and 7.58%, respectively.image