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http://dx.doi.org/10.7848/ksgpc.2021.39.3.133

Automatic Construction of Deep Learning Training Data for High-Definition Road Maps Using Mobile Mapping System  

Choi, In Ha (Dept. of Spatial Information Engineering, Namseoul University)
Kim, Eui Myoung (Dept. of Drone.GIS Engineering, Namseoul University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.3, 2021 , pp. 133-139 More about this Journal
Abstract
Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.
Keywords
PointNet; Deep Learning; Automatic Construction of Training Data; High-Definition Road Maps; Point Cloud;
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