Automated pavement distress detection using region based convolutional neural networks

ABSTRACT

Automatic pavement crack detection is essential for evaluating maintenance requirements and ensuring driving safety. Crack detection plays a primary role in realising the automatic evaluation of pavement condition. Most existing researches on pavement crack detection rely on laborious work, which is a time- and cost-intensive process. Although there has been considerable research on pavement crack detection, it remains a challenging task owing to diverse complex pavement conditions. Recently, deep learning-based algorithms have achieved significant success in computer vision tasks. However, the techniques still have limitations for automatic pavement distress detection. To overcome the current limitations, this study proposes a method for detecting signs of pavement distress based on faster region based convolutional neural network (Faster R-CNN). The study focuses on the detection of longitudinal cracks, transverse cracks, alligator cracks, and partial patching in pavement images. A framework for applying the Faster R-CNN technique to a full-size pavement image is also proposed, which allows the sliding window size to be reduced, thus enabling the detection of larger images. The performance of the proposed method was validated against a dataset containing actual pavement images. The experimental test results show that the proposed method could successfully detect cracks and partial patching with accuracy.

KEYWORDS:

• Pavement condition evaluation
• Faster R-CNN
• region proposal network
• deep learning
• automated crack detection

Acknowledgements

This research was supported by a grant (20SCIP-C151411-02) from Construction Technology Research Programme funded by the Ministry of Land, Infrastructure and Transport of the Korean government.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Ministry of Land, Infrastructure and Transport [grant number 20SCIP-C151411-02].