RT Journal Article
T1 Low-cost mobile mapping system solution for traffic sign segmentation using Azure Kinect
A1 Qiu, Zhouyan
A1 Martínez Sánchez, Joaquín
A1 Brea Sánchez, Víctor Manuel
A1 López Martínez, Paula
A1 Arias, Pedro
K1 Mobile mapping system (MMS)
K1 Azure Kinect
K1 Time of Flight camera
K1 Traffic sign detection and segmentation
K1 Multi sensor system
K1 Mobile mapping system
AB The mobile mapping system (MMS) could become the foundation of digital twins and 3D modeling, and is widely applicable in a variety of fields, such as infrastructure management, intelligent transportation systems, and smart cities. However, data collected by MMS is extensive and complex, making data processing difficult. We present a novel method for segmenting urban assets (specifically in this case study traffic signs) with a lower-cost Azure Kinect and automatic data processing workflows. First, it was necessary to verify the reliability of this approach using the Time of Flight (ToF) camera from Azure Kinect to detect road signs outdoors. Using the data generated by the ToF camera, we then extracted the Region of Interest (ROI) quickly and efficiently. After transforming the ROI to the RGB image, we obtained the traffic sign area through a hybrid color-shape based method. In addition, we calculated the distance between the traffic sign and Azure Kinect based on the depth image. The Coefficient of Variation cv averaged 1.1%. It is thus evident that Azure Kinect is reliable for outdoor traffic sign segmentation. Our algorithm has been compared with deep learning algorithms. According to our analysis, our algorithm has an accuracy of 0.8216, while the accuracy of deep learning is 0.7466, which indicates that our solution is more flexible and cost-effective.
PB Elsevier
SN 1569-8432
YR 2022
FD 2022-07-01
LK https://hdl.handle.net/10347/43659
UL https://hdl.handle.net/10347/43659
LA eng
NO International Journal of Applied Earth Observation and Geoinformation Volume 112, August 2022, 102895
NO This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 860370. This work has also received funding from the European Regional Development Fund: from the Xunta de Galicia-Consellería de Cultura, Educación e Ordenación Universitaria Accreditation 2019–2022 ED431G-2019/04 and Reference Competitive Group Accreditation 2021–2024, GRC2021/48. This work has been supported by Gobierno de España: Ministerio de Ciencia, Innovación y Universidades through the Grant PID2019-108816RB-I00 funded by MCIN/AEI/ 10.13039/501100011033.
DS Minerva
RD 22 abr 2026