Road Crack Detection Using U-Net

Recently, an efficient and automatic infrastructure maintenance service is mandatory. To address this demand, in this paper, we introduce a segmentation-based road damage detection method by using U-Net. To train the model, we collect 4K images by using a smartphone mounted on a bicycle and build our own road damage dataset. In addition, to improve detection accuracy, we apply focal loss and image patch for loss function and input image, respectively. From the evaluation, the result confirms that the method demonstrates to extract road damages with acceptable accuracy.