Faster region convolutional neural network for automated pavement distress detection

Pavement images have been utilised to detect distresses. However, existing methods for detecting pavement distresses are not acceptable owing to the various real-world conditions. To complete the task, a novel detection method based on faster region convolutional neural network (Faster R-CNN) was utilised to recognise and locate pavement distresses including crack, pothole, oil bleeding and dot surface autonomously. Twenty Faster R-CNNs were trained and tested by 6498 pavement images. Then the performance of training was analysed to select the optimal Faster R-CNN. At last, a test and a comparative study were presented to verify the stability and superiority of the optimal one. In the testing, average results of the accuracy rates, recall rates and location errors in the optimal one were 90.4%, 89.1% and 6.521 pixels, which were close to average results of training and validation. It indicated the optimal Faster R-CNN had good performance to detect distresses in different pavements. Compared with the CNN and K-value method, the optimal Faster R-CNN located pavement distresses with bounding boxes more precisely.

Automated summary

The study proposed a novel method for detecting pavement distresses based on Faster R-CNN, which was trained and tested on a large dataset of pavement images to select the optimal model. The optimal Faster R-CNN demonstrated good performance in detecting distresses across different pavements, with more precise localization compared to CNN and K-value methods.