A Multitask Fusion Network for Region-Level and Pixel-Level Pavement Distress Detection

Abstract

With the development of state-of-the-art algorithms, pavement distress can already be detected automatically. However, most pavement distress detection is currently implemented as a single task, either at the region level or at the pixel level. To comprehensively assess the pavement condition, a multitask fusion model, Pavement Distress Detection Network (PDDNet), was proposed for integrated pavement distress detection at both the region level and pixel level. PDDNet was trained and tested on distress images captured via unmanned aerial vehicle (UAV), and seven types of pavement distresses were investigated and analyzed. Compared with Mask Region-based Convolutional Neural Network (R-CNN), U-Net, and W-segnet, PDDNet shows higher performance in classification, localization, and segmentation of pavement distresses. Results demonstrate that PDDNet achieves region-level and pixel-level detection of seven types of distresses with the mean average precision of 0.810 and 0.795, respectively. As a portable and lightweight device, the UAV can collect full-width pavement distress images, which helps improve the efficiency of pavement distress detection.