Road Damage Detection using YOLO with Image Tiling about Multi-source Images
Dongjun Jeong, Jua Kim
Abstract
The importance of road damage detection work is continuously increasing, and various methods are being developed to reduce the cost of time and economy. Therefore, the road damage dataset was collected from six countries, such as China, the Czech Republic, India, Japan, Norway, and United States, by various sources such as drones, cars, and motorbikes for making a robust and powerful automatic road state monitoring system. We solved the road damage detection task using YOLO, a deep learning based technology. We adopted the image tiling technique to properly use the high resolution road damage images captured in Norway with other similar size resolution images and trained twelve YOLOv5x models to use the ensemble method for detecting the four road damage types. Finally, our solution obtained an average F1 score of 0.6744 and an inference speed of 1 FPS.