| Abstract |
Roads are important parts of infrastructure. The detection of road condition plays an important role for the traffic safety. Vehicles, weather and other factors will cause different types of damage to the road surface. To avoid this happening, the commonly used method is manual inventory at present, which is time-consuming, laborious and easily leads to omissions. In order to overcome these shortcomings, this paper presents a method of road damage detection based on machine vision, which is more efficient and relatively cheap. To realize the method, the author used the Raspberry Pi, acceleration sensor, GPS module, Neural Compute Stick and camera to complete the design of intelligent inspection terminal. Then the author investigated the common types of road damage, including long strip cracks, reticulation cracks, potholes, and rutting. After that, an SSD-mobilenet architecture was modified and a database including a large number of images for different types of damage was built. The SSD-mobilenet was trained and validated with the built database. Transplanting the SSD-mobilenet to the intelligent inspection terminal, which could realize the road damage detection based on machine vision. The result shows 80.87% average precision (AP) ratings for different types of damage and proves the proposed method is effective. © 2020 SPIE. |
