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Implementing textural features on GPUs for improved real-time pavement distress detection

Doycheva, Kristina; Koch, Christian; K�nig, Markus

Authors

Kristina Doycheva

Christian Koch

Markus K�nig



Abstract

The condition of municipal roads has deteriorated considerably in recent years, leading to large scale pavement distress such as cracks or potholes. In order to enable road maintenance, pavement distress should be timely detected. However, manual investigation, which is still the most widely applied approach toward pavement assessment, puts maintenance personnel at risk and is time-consuming. During the last decade, several efforts have been made to automatically assess the condition of the municipal roads without any human intervention. Vehicles are equipped with sensors and cameras in order to collect data related to pavement distress and record videos of the pavement surface. Yet, this data are usually not processed while driving, but instead it is recorded and later analyzed off-line. As a result, a vast amount of memory is required to store the data and the available memory may not be sufficient. To reduce the amount of saved data, the authors have previously proposed a graphics processing units (GPU)-enabled pavement distress detection approach based on the wavelet transform of pavement images. The GPU implementation enables pavement distress detection in real time. Although the method used in the approach provides very good results, the method can still be improved by incorporating pavement surface texture characteristics. This paper presents an implementation of textural features on GPUs for pavement distress detection. Textural features are based on gray-tone spatial dependencies in an image and characterize the image texture. To evaluate the computational efficiency of the GPU implementation, performance tests are carried out. The results show that the speedup achieved by implementing the textural features on the GPU is sufficient to enable real-time detection of pavement distress. In addition, classification results obtained by applying the approach on 16,601 pavement images are compared to the results without integrating textural features. There results demonstrate that an improvement of 27% is achieved by incorporating pavement surface texture characteristics.

Citation

Doycheva, K., Koch, C., & König, M. (in press). Implementing textural features on GPUs for improved real-time pavement distress detection. Journal of Real-Time Image Processing, https://doi.org/10.1007/s11554-016-0648-1

Journal Article Type Article
Acceptance Date Oct 11, 2016
Online Publication Date Oct 24, 2016
Deposit Date Oct 27, 2016
Publicly Available Date Mar 28, 2024
Journal Journal of Real-Time Image Processing
Print ISSN 1861-8200
Electronic ISSN 1861-8219
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1007/s11554-016-0648-1
Keywords Pavement distress detection; Textural features; Haralick features; Graphics processing units
Public URL https://nottingham-repository.worktribe.com/output/822008
Publisher URL http://link.springer.com/article/10.1007%2Fs11554-016-0648-1
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/s11554-016-0648-1

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