| description abstract | Pavement distress detection is of great significance to pavement maintenance and management. Currently, image-processing methods have been developed, especially with the widespread use of the convolutional neural networks (CNN). However, most CNN applications are still limited to the general object detection model (GODM), which is difficult to adapt to the detection of complex road scenes. In this study, a dedicated object detection model, RoadID, was designed based on the CNN to detect multiple pavement distresses in a complex environment. In terms of training data, a natural pavement distress data set was established, in which eight pavement distress types—distress, crack, patch-crack, net-crack, patch-net, pothole, patch-pothole, manhole, and hinged-joint—were annotated. More than 60,000 images and 140,000 distresses are described in the data set. Various data augmentation methods, such as motion blur, frequency noise, and random rotation, were used to expand the original data set about 2.5 times. Resnet152 was chosen as the basic feature extraction network and pretrained with manhole images, which promoted the expression ability of the model and the convergence efficiency of training. To improve the imbalance of positive and negative samples, focal loss and mean squared error were combined as loss functions for training. In the prediction stage, modular design was adapted for each distress category for convenience of the expansion of other detection categories, enhancing the scalability of RoadID. Road markings and shadows were added to the data training as disturbances to improve the generalization performance of the model in complex scenes. The mean accuracy of the model reached 83.5%. Compared with the GODM, RoadID has advantages in both recognition accuracy and prediction speed. The proposed method can be used for rapid investigation of pavement conditions to support maintenance and repair. | |
