dc.contributor.author | Hoseini, Mostafa | |
dc.contributor.author | Puliti, Stefano | |
dc.contributor.author | Hoffmann, Stephan | |
dc.contributor.author | Astrup, Rasmus Andreas | |
dc.date.accessioned | 2024-01-17T14:53:20Z | |
dc.date.available | 2024-01-17T14:53:20Z | |
dc.date.created | 2023-12-19T13:26:42Z | |
dc.date.issued | 2023-12-18 | |
dc.identifier.citation | Hoseini, M., Puliti, S., Hoffmann, S., & Astrup, R. (2023). Pothole detection in the woods: a deep learning approach for forest road surface monitoring with dashcams. International Journal of Forest Engineering, 1–10. | en_US |
dc.identifier.issn | 1494-2119 | |
dc.identifier.uri | https://hdl.handle.net/11250/3112286 | |
dc.description.abstract | Sustainable forest management systems require operational measures to preserve the functional design of forest roads. Frequent road data collection and analysis are essential to support target-oriented and efficient maintenance planning and operations. This study demonstrates an automated solution for monitoring forest road surface deterioration using consumer-grade optical sensors. A YOLOv5 model with StrongSORT tracking was adapted and trained to detect and track potholes in the videos captured by vehicle-mounted cameras. For model training, datasets recorded in diverse geographical regions under different weather conditions were used. The model shows a detection and tracking performance of up to a precision and recall level of 0.79 and 0.58, respectively, with 0.70 mean average precision at an intersection over union (IoU) of at least 0.5. We applied the trained model to a forest road in southern Norway, recorded with a Global Navigation Satellite System (GNSS)−fitted dashcam. GNSS-delivered geographical coordinates at 10 Hz rate were used to geolocate the detected potholes. The geolocation performance over this exemple road stretch of 1 km exhibited a root mean square deviation of about 9.7 m compared to OpenStreetMap. Finally, an exemple road deterioration map was compiled, which can be used for scheduling road maintenance operations. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Informa UK Limited | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Pothole detection in the woods: a deep learning approach for forest road surface monitoring with dashcams | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | © 2023 The Author(s) | en_US |
dc.source.journal | International Journal of Forest Engineering | en_US |
dc.identifier.doi | https://doi.org/10.1080/14942119.2023.2290795 | |
dc.identifier.cristin | 2215600 | |
cristin.ispublished | false | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |