Automatic Label Creation Framework for FMCW Radar Images Using Camera Data
Metadatos
Mostrar el registro completo del ítemEditorial
IEEE
Materia
Sensor fusion Machine learning algorithms Deep learning Radar Auto-labeling system
Fecha
2021-06-07Referencia bibliográfica
J. Mendez, S... [et al.]. "Automatic Label Creation Framework for FMCW Radar Images Using Camera Data," in IEEE Access, vol. 9, pp. 83329-83339, 2021, doi: [10.1109/ACCESS.2021.3087207]
Patrocinador
Project A-SWARM through the German Federal Ministry of Economy and Industry (BMWI) by the Maritime Forschungsstrategie 2025 03SX485DResumen
Data acquisition and treatment are key issues for any Deep Learning (DL) technique, especially
in computer vision tasks. A big effort must be done for the creation of labeled datasets, due to the time this
task requires and its complexity in cases where different sensors must be used. This is the case of radar
imaging applications, where radar data are dif cult to analyze and must be labeled manually. In this paper,
a semi-automatic framework to generate labels for range Doppler maps (radar images) is proposed. This
technique is based on a sensor fusion approach with radar and camera sensors. The proposed scheme operates
in two steps: The rst step is the environment features extraction, in which the radar data is preprocessed and
ltered to remove ghost targets and detect clusters, and camera data are used to extract the information of the
targets. In the second step, a rule-based system that considers the extracted features fuses the information to
generate labels for the radar data. By using the proposed framework, the experimentation performed suggests
that the time required to label the data is reduced as well as the possibility of human error during the labeling
task. Our results show that the proposed technique can improve the nal model accuracy with regards the
traditional labeling method, carried out by human experts.