Abstract
Semantic segmentation is essential for autonomous driving, which classifies roads and other objects in the image and provides pixel-level information. For high quality autonomous driving, it is necessary to consider the driving environment of the vehicle, and the vehicle speed should be controlled according to types of road. For this purpose, the semantic segmentation module has to classify types of road. However, current public datasets do not provide annotation data for these road types. In this paper, we propose a method to train the semantic segmentation model for classifying road types. We analyzed the problems that can occur when using a public dataset like KITTI or Cityscapes for training, and used Mapillary Vistas data as training data to get generalized performance. In addition, we use focal loss and over-sampling techniques to alleviate the class imbalance problem caused by relatively small class data.
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Acknowledgments
This work was in parts supported by Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (No. 20000293, Road Surface Condition Detection using Environmental and In-vehicle Sensors).
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Lee, D., Kim, S., Lee, H., Chung, C.C., Kim, WY. (2020). Paved and Unpaved Road Segmentation Using Deep Neural Network. In: Cree, M., Huang, F., Yuan, J., Yan, W. (eds) Pattern Recognition. ACPR 2019. Communications in Computer and Information Science, vol 1180. Springer, Singapore. https://doi.org/10.1007/978-981-15-3651-9_3
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DOI: https://doi.org/10.1007/978-981-15-3651-9_3
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