Detection of asphalt roads degradation using Deep Learning applied to Unmanned Aerial Vehicle imagery
Publication Type
Conference Proceeding
Publication Date (Issue Year)
2023
Journal Name
International Conference on Intelligent Computing and Next Generation Networks（ICNGN)
Abstract
Asphalt roads deteriorate over time due to wear and tear, weather conditions and the effect of traffic loads. These degradations cause enormous damage to road users and economic losses to countries. In Senegal, the inspection of roads for maintenance purposes is done by field surveys and measurements, which is tedious, slow and expensive. This paper proposes a solution for automatic detection of the degraded state of paved roads using Deep Learning applied to drone imagery. The methodology includes three phases: collection of pavement images by drone, processing (annotation, training and test) of the images by YOLOv8 and localization of degraded areas on GeoTiff results of reconstructed pavements. The model was trained and tested on a dataset with a wide range of pavement images and the results show a precision rate of 86.7%, a recall rate of 78.8% and an F1 score of 82.5%.
Keywords
Degradation, Deep learning, Training, Asphalt, Roads, Telecommunication traffic, Drones
Rsif Scholar Name
Adama Coulibaly
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Gaston Berger (UGB), Senegal
Recommended Citation
Coulibaly, A., Ngom, I., Dembélé, J. M., Sadio, O., Tall, M. M., & Diagne, I. (2023). Detection of asphalt roads degradation using Deep Learning applied to Unmanned Aerial Vehicle imagery. International Conference on Intelligent Computing and Next Generation Networks（ICNGN), 2023, 1-5. https://doi.org/10.1109/ICNGN59831.2023.10396763