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Pregled bibliografske jedinice broj: 1207924

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images


Brkić, Ivan; Miler, Mario; Ševrović, Marko; Medak, Damir
Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images // Sensors, 22 (2022), 15; 55100, 17 doi:10.3390/s22155510 (međunarodna recenzija, članak, znanstveni)


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Naslov
Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

Autori
Brkić, Ivan ; Miler, Mario ; Ševrović, Marko ; Medak, Damir

Izvornik
Sensors (1424-8220) 22 (2022), 15; 55100, 17

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Lidar ; road safety ; road assessment ; roadside features

Sažetak
The United Nations (UN) stated that all new roads and 75% of travel time on roads must be 3+ star standard by 2030. The number of stars is determined by the International Road Assessment Program (iRAP) star rating module. It is based on 64 attributes for each road. In this paper, a framework for highly accurate and fully automatic determination of two attributes is proposed: roadside severity-object and roadside severity- distance. The framework integrates mobile Lidar point clouds with deep learning-based object detection on road cross-section images. The You Only Look Once (YOLO) network was used for object detection. Lidar data were collected by vehicle-mounted mobile Lidar for all Croatian highways. Point clouds were collected in .las format and cropped to 10 m-long segments align vehicle path. To determine both attributes, it was necessary to detect the road with high accuracy, then roadside severity-distance was determined with respect to the edge of the detected road. Each segment is finally classified into one of 13 roadside severity object classes and one of four roadside severity-distance classes. The overall accuracy of the roadside severity-object classification is 85.1%, while for the distance attribute it is 85.6%. The best average precision is achieved for safety barrier concrete class (0.98), while the worst AP is achieved for rockface class (0.72).

Izvorni jezik
Engleski



POVEZANOST RADA


Ustanove:
Geodetski fakultet, Zagreb,
Fakultet prometnih znanosti, Zagreb

Profili:

Avatar Url Mario Miler (autor)

Avatar Url Marko Ševrović (autor)

Avatar Url Damir Medak (autor)

Avatar Url Ivan Brkić (autor)

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Brkić, Ivan; Miler, Mario; Ševrović, Marko; Medak, Damir
Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images // Sensors, 22 (2022), 15; 55100, 17 doi:10.3390/s22155510 (međunarodna recenzija, članak, znanstveni)
Brkić, I., Miler, M., Ševrović, M. & Medak, D. (2022) Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images. Sensors, 22 (15), 55100, 17 doi:10.3390/s22155510.
@article{article, author = {Brki\'{c}, Ivan and Miler, Mario and \v{S}evrovi\'{c}, Marko and Medak, Damir}, year = {2022}, pages = {17}, DOI = {10.3390/s22155510}, chapter = {55100}, keywords = {Lidar, road safety, road assessment, roadside features}, journal = {Sensors}, doi = {10.3390/s22155510}, volume = {22}, number = {15}, issn = {1424-8220}, title = {Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images}, keyword = {Lidar, road safety, road assessment, roadside features}, chapternumber = {55100} }
@article{article, author = {Brki\'{c}, Ivan and Miler, Mario and \v{S}evrovi\'{c}, Marko and Medak, Damir}, year = {2022}, pages = {17}, DOI = {10.3390/s22155510}, chapter = {55100}, keywords = {Lidar, road safety, road assessment, roadside features}, journal = {Sensors}, doi = {10.3390/s22155510}, volume = {22}, number = {15}, issn = {1424-8220}, title = {Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images}, keyword = {Lidar, road safety, road assessment, roadside features}, chapternumber = {55100} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


Citati:





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