Abstract
As semantic segmentation models can accurately distinguish object category and contour from the background of images, its application in autonomous driving has been widely studied in recent years. As one of the most important perception modules in autonomous driving, many modern lane line detection models adopt the method of semantic segmentation or instance segmentation. Compared to other objects in traffic scenes, lane lines have a linear structure characteristic of regular shape. Based on that, we design a semantic segmentation model to take advantage of the special structure of lane lines. Combining VH-stage’s two-branches horizontal and vertical one-dimensional convolution with SCNN’s spatial convolution, we propose a multi spatial convolution block (MSCB). We evaluate our method on PSV and TSD datasets and find MSCB substantially improves the accuracy of lane lines semantic segmentation by up to 4%.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (No.U19A2069).
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Wu, Y., Liu, F., Jiang, W., Yang, X. (2021). Multi Spatial Convolution Block for Lane Lines Semantic Segmentation. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_3
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DOI: https://doi.org/10.1007/978-3-030-84529-2_3
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