Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images
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 hig...
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| Published in: | Sensors (Basel, Switzerland) Vol. 22; no. 15; p. 5510 |
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| Abstract | 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). |
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| AbstractList | 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). |
| Author | Medak, Damir Brkić, Ivan Ševrović, Marko Miler, Mario |
| AuthorAffiliation | 1 Department of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia; ibrkic@geof.unizg.hr (I.B.); dmedak@geof.unizg.hr (D.M.) 2 Department of Transport Planning, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia; msevrovic@fpz.unizg.hr |
| AuthorAffiliation_xml | – name: 2 Department of Transport Planning, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia; msevrovic@fpz.unizg.hr – name: 1 Department of Geoinformatics, Faculty of Geodesy, University of Zagreb, Kačićeva 26, 10000 Zagreb, Croatia; ibrkic@geof.unizg.hr (I.B.); dmedak@geof.unizg.hr (D.M.) |
| Author_xml | – sequence: 1 givenname: Ivan orcidid: 0000-0003-0301-0485 surname: Brkić fullname: Brkić, Ivan – sequence: 2 givenname: Mario orcidid: 0000-0003-3883-8493 surname: Miler fullname: Miler, Mario – sequence: 3 givenname: Marko surname: Ševrović fullname: Ševrović, Marko – sequence: 4 givenname: Damir surname: Medak fullname: Medak, Damir |
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| SubjectTerms | Accuracy Automation Classification Cross-sections Data collection Deep learning Fatalities Infrastructure Lidar Neural networks road assessment road safety Roads & highways roadside features Safety barriers Segments Sensors Traffic accidents & safety Traffic flow Travel time Unmanned aerial vehicles |
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| Title | Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images |
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