Rural road environment segmentation of LiDAR dataset with deep learning

Zakria; Jianhua Deng; Jiani He; Jingye Cai; Muhammad Saddam Khokhar

doi:10.1117/12.2631445

1 May 2022 Rural road environment segmentation of LiDAR dataset with deep learning

Zakria, Jianhua Deng, Jiani He, Jingye Cai, Muhammad Saddam Khokhar

Proceedings Volume 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021); 121710F (2022) https://doi.org/10.1117/12.2631445
Event: Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 2021, Shanghai, China

Abstract

Unstructured road segmentation is a key task in self-driving technology and it’s still a challenging problem. Mostly available point cloud datasets focus on data collected from urban areas, and approaches are evaluated for structured roads or urban areas, which has considerable limitations in rural areas such as fails at night, road without boundary lines, and no markings. In this regard, we present a new large-scale aerial LiDAR dataset of rural roads with hand-labeled points spanning 500 km² of road and nine object categories. Our dataset is the most extensive dataset contains a critical number of expert-verified hand-labeled points for analyzing 3D deep learning algorithms, allowing existing algorithms to shift their focus to unstructured road data. The nature of our data, the annotation methodology, and the performance of existing state-of-the-art algorithms on our dataset are all described in detail. Furthermore, challenges and applications of rural area road semantic segmentation are discussed in detail.

Citation Download Citation

Zakria, Jianhua Deng, Jiani He, Jingye Cai, and Muhammad Saddam Khokhar "Rural road environment segmentation of LiDAR dataset with deep learning", Proc. SPIE 12171, Thirteenth International Conference on Signal Processing Systems (ICSPS 2021), 121710F (1 May 2022); https://doi.org/10.1117/12.2631445

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