Automatic Defect Detection of Pavement Diseases

Abstract

Pavement disease detection is an important task for ensuring road safety. Manual visual detection requires a significant amount of time and effort. Therefore, an automated road disease identification technique is required to guarantee that city tasks are performed. However, due to the irregular shape and large-scale differences in road diseases, as well as the imbalance between the foreground and background, the task is challenging. Because of this, we created the deep convolution neural network—DASNet, which can be used to identify road diseases automatically. The network employs deformable convolution instead of regular convolution as the feature pyramid’s input, adds the same supervision signal to the multi-scale features before feature fusion, decreases the semantic difference, extracts context information by residual feature enhancement, and reduces the information loss of the pyramid’s top-level feature map. Considering the unique shape of road diseases, imbalance problems between the foreground and background are common, therefore, we introduce the sample weighted loss function. In order to prove the superiority and effectiveness of this method, it is compared to the latest method. A large number of experiments show that this method is superior in accuracy to other methods, specifically, under the COCO evaluation metric, compared with the Faster RCNN baseline, the proposed method obtains a 41.1 mAP and 3.4 AP improvement.