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Article

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

1
Department of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia
2
Department of Transport Planning, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Academic Editor: Marek Jaśkiewicz
Sensors 2022, 22(15), 5510; https://doi.org/10.3390/s22155510
Received: 21 June 2022 / Revised: 15 July 2022 / Accepted: 21 July 2022 / Published: 23 July 2022
(This article belongs to the Section Sensing and Imaging)
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). View Full-Text
Keywords: Lidar; road safety; road assessment; roadside features Lidar; road safety; road assessment; roadside features
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MDPI and ACS Style

Brkić, I.; Miler, M.; Ševrović, M.; Medak, D. Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images. Sensors 2022, 22, 5510. https://doi.org/10.3390/s22155510

AMA Style

Brkić I, Miler M, Ševrović M, Medak D. Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images. Sensors. 2022; 22(15):5510. https://doi.org/10.3390/s22155510

Chicago/Turabian Style

Brkić, Ivan, Mario Miler, Marko Ševrović, and Damir Medak. 2022. "Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images" Sensors 22, no. 15: 5510. https://doi.org/10.3390/s22155510

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