Automatic Recognition and Geolocation of Vertical Traffic Signs Based on Artificial Intelligence Using a Low-Cost Mapping Mobile System
Autor(en): |
Hugo Domínguez
Alberto Morcillo Mario Soilán Diego González-Aguilera |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Infrastructures, Oktober 2022, n. 10, v. 7 |
Seite(n): | 133 |
DOI: | 10.3390/infrastructures7100133 |
Abstrakt: |
Road maintenance is a key aspect of road safety and resilience. Traffic signs are an important asset of the road network, providing information that enhances safety and driver awareness. This paper presents a method for the recognition and geolocation of vertical traffic signs based on artificial intelligence and the use of a low-cost mobile mapping system. The approach developed includes three steps: First, traffic signals are detected and recognized from imagery using a deep learning architecture with YOLOV3 and ResNet-152. Next, LiDAR point clouds are used to provide metric capabilities and cartographic coordinates. Finally, a WebGIS viewer was developed based on Potree architecture to visualize the results. The experimental results were validated on a regional road in Avila (Spain) demonstrating that the proposed method obtains promising, accurate and reliable results. |
Copyright: | © 2022 the Authors. Licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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10722811 - Veröffentlicht am:
22.04.2023 - Geändert am:
10.05.2023