Pixel-wise Road Pavement Defects Detection Using U-Net Deep Neural Network

Abstract

Textured surface defects detection can be a complicated task. Maintenance and monitoring of big infrastructures, such as roads, is expensive, time-consuming, and requires many human resources. In many areas, humans are being replaced by computer vision systems that perform faster and more precise. Moreover, some inspection tasks can reach incredibly high levels of complexity. Recently, deep learning approaches showed impressive results in object detection and image segmentation. It can provide a state-of-the-art solution for most computer vision tasks, including pattern inspection and defect detection. In this work, we present a pixel-wise road pavement defects detection method by using U-Net convolutional neural network. We have experimentally evaluated the impact of a different number of layers, filter sizes and the number of features in segmentation performance and processing time. The best-suggested configuration for road pavement cracks segmentation task has received up to 98.92% of accuracy in 0.049 s per image.