Araştırma Makalesi
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İHA Kullanarak Yol Bozukluk Ölçmeleri

Yıl 2020, Cilt: 1 Sayı: 1, 13 - 23, 31.03.2020

Öz

Yolun bakımı ve iyileştirilmesi çok önemli eylemlerdir. Bu nedenle, bu eylemler yapılmadan önce yol koşulları doğru bir şekilde incelenmelidir. Arazideki manuel ve görsel olarak yapılan inceleme, yol durumunu izlemek için geleneksel olarak kullanılan yöntemdir. Ancak, bu, zaman alıcı, yoğun emek isteyen ve maliyetli bir yöntemdir. Buna ek olarak, geleneksel inceleme yöntemi sadece denetleyiciler için değil aynı zamanda yayalar ve sürücüler gibi yolun birincil kullanıcıları için de güvenlik problemi oluşturur. Bu çalışmada, yol durumunun incelenmesi için insansız hava aracı (İHA) kullanılmaktadır. İHA teknolojisi, verileri verimli ve doğru bir şekilde toplamak için önemli bir araç haline gelmektedir. Önerilen yöntem üç adımdan oluşur. İlk olarak, İHA uçuşundan birçok görüntü elde edilir. Daha sonra, görüntüler, üç boyutlu (3B) nokta bulutu, sayısal yüzey modeli ve ortomozaik oluşturmak için kullanılır. Son olarak, iki boyutlu (2B) ve 3B verilerden yol bozuklukları tespit edilir ve ölçülür. Önerilen metodolojiden elde edilen ölçüler, geleneksel inceleme yönteminden alınan ölçülerle karşılaştırılmıştır. Her iki ölçümden de nispeten benzer sonuçlar elde edilmiştir. Sonuç olarak, İHA ölçüm tekniğinin kullanımı yol bozukluklarını tespit etmek için uygundur. Önerilen yöntemin avantajları göz önüne alındığında, geleneksel inceleme yöntemi yerine İHA'nın kullanılabileceği sonucuna varmak oldukça güvenilirdir.

Kaynakça

  • Buğday, E. (2018). Capabilities of using UAVs in forest road construction activities. European Journal of Forest Engineering, 4(2), 56-62. doi:10.33904/ejfe.499784.
  • Desa, H., bin Azizan, M. A., Khadir, M. S. A., Suhaimi, M. S., Ramli, N. Z., & Hat, Z. (2019). Feasibility Study of UAV Implementation in Route Surveying. Journal of Robotics, Networking and Artificial Life, 6(2), 84-88. doi:10.2991/jrnal.k.190828.003.
  • Doshi, A. A., Postula, A. J., Fletcher, A., & Singh, S. P. (2015). Development of micro-UAV with integrated motion planning for open-cut mining surveillance. Microprocessors and Microsystems, 39(8), 829-835.
  • Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., & Balakrishnan, H. (2008, June). The pothole patrol: using a mobile sensor network for road surface monitoring. In Proceedings of the 6th international conference on Mobile systems, applications, and services (pp. 29-39).
  • Feng, Q., Liu, J., & Gong, J. (2015). Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—A case of Yuyao, China. Water, 7(4), 1437-1455.
  • Fryskowska, A. (2019). Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis. Sensors, 19(3), 700. doi:10.3390/s19030700.
  • Gezero, L., & Antunes, C. (2019). Road Rutting Measurement Using Mobile LiDAR Systems Point Cloud. ISPRS International Journal of Geo-Information, 8(9), 404. doi:10.3390/ijgi8090404.
  • Gulci, S. (2019). The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies: an analysis of data production in pure coniferous young forest stands. Environmental monitoring and assessment, 191(8), 495. doi:10.1007/s10661-019-7628-4.
  • Inzerillo, L., Di Mino, G., & Roberts, R. (2018). Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 457-469. doi:10.1016/j.autcon.2018.10.010.
  • Javernick, L., Brasington, J., & Caruso, B. (2014). Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology, 213, 166-182. doi:10.1016/j.geomorph.2014.01.006.
  • Johansen, K., Erskine, P. D., & McCabe, M. F. (2019). Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines. Journal of cleaner production, 209, 819-833. doi:10.1016/j.jclepro.2018.10.287.
  • Kim, T., & Ryu, S. K. (2014). Review and analysis of pothole detection methods. Journal of Emerging Trends in Computing and Information Sciences, 5(8), 603-608.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110. doi:10.1023/b:Visi.0000029664.99615.94.
  • Neupane, S. R., & Gharaibeh, N. G. (2019). A heuristics-based method for obtaining road surface type information from mobile lidar for use in network-level infrastructure management. Measurement, 131, 664-670. doi:10.1016/j.measurement.2018.09.015.
  • Ochoa, K. S., & Guo, Z. (2019). A framework for the management of agricultural resources with automated aerial imagery detection. Computers and Electronics in Agriculture, 162, 53-69. doi:10.1016/j.compag.2019.03.028.
  • Peppa, M. V., Hall, J., Goodyear, J., & Mills, J. P. (2019). Photogrammetric assessment and comparison of DJI Phantom 4 pro and phantom 4 RTK small unmanned aircraft systems. ISPRS Geospatial Week 2019.
  • Pijl, A., Tosoni, M., Roder, G., Sofia, G., & Tarolli, P. (2019). Design of Terrace drainage networks using UAV-based high-resolution topographic data. Water, 11(4), 814. doi:10.3390/w11040814.
  • Saad, A. M., & Tahar, K. N. (2019). Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV). Measurement, 137, 647-654. doi:10.1016/j.measurement.2019.01.093.
  • Tan, Y., & Li, Y. (2019). UAV Photogrammetry-Based 3D Road Distress Detection. ISPRS International Journal of Geo-Information, 8(9), 409. doi:10.3390/ijgi8090409.
  • Tomastik, J., Mokroš, M., Surový, P., Grznárová, A., & Merganič, J. (2019). UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote sensing, 11(6), 721. doi:10.3390/rs11060721.
  • Yadav, M., & Singh, A. K. (2018). Rural road surface extraction using mobile LiDAR point cloud data. Journal of the Indian Society of Remote Sensing, 46(4), 531-538. doi:10.1007/s12524-017-0732-4.
  • Yıldızel, S. A., & Calış, G. (2019). Unmanned Aerial Vehicles for Civil Engineering: Current Practises and Regulations. Avrupa Bilim ve Teknoloji Dergisi, (16), 925-932. doi:10.31590/ejosat.565499.
  • Zhang, C. (2008). An UAV-based photogrammetric mapping system for road condition assessment. International Archives of the Photogrammetry. Remote Sensing Spatial Information Sciences. Sci, 37, 627-632.

Road Distress Measurements Using UAV

Yıl 2020, Cilt: 1 Sayı: 1, 13 - 23, 31.03.2020

Öz

Maintenance and rehabilitation of the road are very serious actions. Therefore, road conditions should be inspected accurately before taking these actions. Manual and visual inspection in the field is the traditionally used method to monitor road conditions. However, it is time-consuming, labor-intense and costly. In addition, the traditional inspection method is unsafe directly for the inspectors and indirectly for primary users of the road, such as pedestrians and drivers. In this study, the unmanned aerial vehicle (UAV) was used to inspect the road condition. UAV technology is becoming a valuable tool for collecting data efficiently and accurately. The proposed method involved three steps. First, several images were acquired from a UAV flight. Then, these images were used to generate a three-dimensional (3D) point cloud, digital surface model and orthomosaic. Finally, road distresses were detected and measured from two-dimensional (2D) and 3D data. The measurements obtained from the proposed methodology were compared against the measurements obtained from the traditional inspection method. It was found that both measurements produced similar results. In conclusion, the use of the UAV measurement technique was found to be suitable for detecting road distress. Given the advantages of the proposed methodology, it can also be inferred that UAVs can be used instead of the traditional inspection method.

Kaynakça

  • Buğday, E. (2018). Capabilities of using UAVs in forest road construction activities. European Journal of Forest Engineering, 4(2), 56-62. doi:10.33904/ejfe.499784.
  • Desa, H., bin Azizan, M. A., Khadir, M. S. A., Suhaimi, M. S., Ramli, N. Z., & Hat, Z. (2019). Feasibility Study of UAV Implementation in Route Surveying. Journal of Robotics, Networking and Artificial Life, 6(2), 84-88. doi:10.2991/jrnal.k.190828.003.
  • Doshi, A. A., Postula, A. J., Fletcher, A., & Singh, S. P. (2015). Development of micro-UAV with integrated motion planning for open-cut mining surveillance. Microprocessors and Microsystems, 39(8), 829-835.
  • Eriksson, J., Girod, L., Hull, B., Newton, R., Madden, S., & Balakrishnan, H. (2008, June). The pothole patrol: using a mobile sensor network for road surface monitoring. In Proceedings of the 6th international conference on Mobile systems, applications, and services (pp. 29-39).
  • Feng, Q., Liu, J., & Gong, J. (2015). Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—A case of Yuyao, China. Water, 7(4), 1437-1455.
  • Fryskowska, A. (2019). Improvement of 3D Power Line Extraction from Multiple Low-Cost UAV Imagery Using Wavelet Analysis. Sensors, 19(3), 700. doi:10.3390/s19030700.
  • Gezero, L., & Antunes, C. (2019). Road Rutting Measurement Using Mobile LiDAR Systems Point Cloud. ISPRS International Journal of Geo-Information, 8(9), 404. doi:10.3390/ijgi8090404.
  • Gulci, S. (2019). The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies: an analysis of data production in pure coniferous young forest stands. Environmental monitoring and assessment, 191(8), 495. doi:10.1007/s10661-019-7628-4.
  • Inzerillo, L., Di Mino, G., & Roberts, R. (2018). Image-based 3D reconstruction using traditional and UAV datasets for analysis of road pavement distress. Automation in Construction, 96, 457-469. doi:10.1016/j.autcon.2018.10.010.
  • Javernick, L., Brasington, J., & Caruso, B. (2014). Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology, 213, 166-182. doi:10.1016/j.geomorph.2014.01.006.
  • Johansen, K., Erskine, P. D., & McCabe, M. F. (2019). Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines. Journal of cleaner production, 209, 819-833. doi:10.1016/j.jclepro.2018.10.287.
  • Kim, T., & Ryu, S. K. (2014). Review and analysis of pothole detection methods. Journal of Emerging Trends in Computing and Information Sciences, 5(8), 603-608.
  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110. doi:10.1023/b:Visi.0000029664.99615.94.
  • Neupane, S. R., & Gharaibeh, N. G. (2019). A heuristics-based method for obtaining road surface type information from mobile lidar for use in network-level infrastructure management. Measurement, 131, 664-670. doi:10.1016/j.measurement.2018.09.015.
  • Ochoa, K. S., & Guo, Z. (2019). A framework for the management of agricultural resources with automated aerial imagery detection. Computers and Electronics in Agriculture, 162, 53-69. doi:10.1016/j.compag.2019.03.028.
  • Peppa, M. V., Hall, J., Goodyear, J., & Mills, J. P. (2019). Photogrammetric assessment and comparison of DJI Phantom 4 pro and phantom 4 RTK small unmanned aircraft systems. ISPRS Geospatial Week 2019.
  • Pijl, A., Tosoni, M., Roder, G., Sofia, G., & Tarolli, P. (2019). Design of Terrace drainage networks using UAV-based high-resolution topographic data. Water, 11(4), 814. doi:10.3390/w11040814.
  • Saad, A. M., & Tahar, K. N. (2019). Identification of rut and pothole by using multirotor unmanned aerial vehicle (UAV). Measurement, 137, 647-654. doi:10.1016/j.measurement.2019.01.093.
  • Tan, Y., & Li, Y. (2019). UAV Photogrammetry-Based 3D Road Distress Detection. ISPRS International Journal of Geo-Information, 8(9), 409. doi:10.3390/ijgi8090409.
  • Tomastik, J., Mokroš, M., Surový, P., Grznárová, A., & Merganič, J. (2019). UAV RTK/PPK Method—An Optimal Solution for Mapping Inaccessible Forested Areas?. Remote sensing, 11(6), 721. doi:10.3390/rs11060721.
  • Yadav, M., & Singh, A. K. (2018). Rural road surface extraction using mobile LiDAR point cloud data. Journal of the Indian Society of Remote Sensing, 46(4), 531-538. doi:10.1007/s12524-017-0732-4.
  • Yıldızel, S. A., & Calış, G. (2019). Unmanned Aerial Vehicles for Civil Engineering: Current Practises and Regulations. Avrupa Bilim ve Teknoloji Dergisi, (16), 925-932. doi:10.31590/ejosat.565499.
  • Zhang, C. (2008). An UAV-based photogrammetric mapping system for road condition assessment. International Archives of the Photogrammetry. Remote Sensing Spatial Information Sciences. Sci, 37, 627-632.
Toplam 23 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Mustafa Zeybek 0000-0001-8640-1443

Serkan Biçici 0000-0002-0621-9324

Yayımlanma Tarihi 31 Mart 2020
Gönderilme Tarihi 22 Ocak 2020
Kabul Tarihi 16 Mart 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 1 Sayı: 1

Kaynak Göster

APA Zeybek, M., & Biçici, S. (2020). Road Distress Measurements Using UAV. Türk Uzaktan Algılama Ve CBS Dergisi, 1(1), 13-23.

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.