Automatic Pothole Detection by Different Multispectral Band Combinations
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-47718-8_23
Reference19 articles.
1. Leonardi, G., Barrile, V., Palamara, R., Suraci, F., Candela, G.: 3D mapping of pavement distresses using an unmanned aerial vehicle (UAV) system. Smart Innov. Syst. Technol. 101, 164–171 (2019). https://doi.org/10.1007/978-3-319-92102-0_18
2. Pan, Y., Zhang, X., Cervone, G., Yang, L.: Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11(10), 3701–3712 (2018). https://doi.org/10.1109/JSTARS.2018.2865528
3. Rakha, T., Gorodetsky, A.: Review of unmanned aerial system (UAS) applications in the built environment: towards automated building inspection procedures using drones. Autom. Constr. 93(September), 252–264 (2018). https://doi.org/10.1016/J.Autcon.2018.05.002
4. Aparna, Bhatia, Y., Rai, R., Gupta, V., Aggarwal, N., Akula, A.: Convolutional neural networks based potholes detection using thermal imaging. J. King Saud Univ. Comput. Inf. Sci. 34(2), 578–588 (2019). https://doi.org/10.1016/J.Jksuci.2019.02.004
5. Hoang, N.D.: An artificial intelligence method for asphalt pavement pothole detection using least squares support vector machine and neural network with steerable filter-based feature extraction. Adv. Civ. Eng. 7419058, 1–12 (2018). https://doi.org/10.1155/2018/7419058
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