Pavement distress detection and classification based on YOLO network

The detection and classification of pavement distress (PD) play a critical role in pavement maintenance and rehabilitation. Research on PD automation detection and measurement has been actively conducted. However, types of PD are more necessary for road managers to take effective actions. Also, lack of a unified PD dataset leads to absence of a benchmark on various methods. This study makes three contributions to address these issues. Firstly, a large-scale PD dataset is prepared. This dataset is composed of 45,788 images captured with a high-resolution industrial camera installed on vehicles, in a variety of weather and illuminance conditions. Each image is annotated with bounding box representing location and type of distress. Secondly, a deep learning-based object detection framework, the YOLO network, is adopted to predict possible distress location and category. Comprehensive detection accuracy reaches 73.64%. The processing speed reaches 0.0347s/pic, as 9 times faster than Faster R-CNN and only 70% of SSD. Finally, the applicability of model under various illumination conditions is also explored. The results reveal that the method significantly outperforms with appropriate illumination. We conclude that the proposed YOLO-based approach is able to detect PD with high accuracy, which requires no manual feature extraction and calculation during detecting.