CurbNet: Curb Detection Framework Based on LiDAR Point Cloud
  Segmentation
Guoyang Zhao Fulong Ma Yuxuan Liu Weiqing Qi Ming Liu
Curb detection is an important function in intelligent driving and can be
used to determine drivable areas of the road. However, curbs are difficult to
detect due to the complex road environment. This paper introduces CurbNet, a
novel framework for curb detection, leveraging point cloud segmentation.
Addressing the dearth of comprehensive curb datasets and the absence of 3D
annotations, we have developed the 3D-Curb dataset, encompassing 7,100 frames,
which represents the largest and most categorically diverse collection of curb
point clouds currently available. Recognizing that curbs are primarily
characterized by height variations, our approach harnesses spatially-rich 3D
point clouds for training. To tackle the challenges presented by the uneven
distribution of curb features on the xy-plane and their reliance on z-axis
high-frequency features, we introduce the multi-scale and channel attention
(MSCA) module, a bespoke solution designed to optimize detection performance.
Moreover, we propose an adaptive weighted loss function group, specifically
formulated to counteract the imbalance in the distribution of curb point clouds
relative to other categories. Our extensive experimentation on 2 major datasets
has yielded results that surpass existing benchmarks set by leading curb
detection and point cloud segmentation models. By integrating multi-clustering
and curve fitting techniques in our post-processing stage, we have
substantially reduced noise in curb detection, thereby enhancing precision to
0.8744. Notably, CurbNet has achieved an exceptional average metrics of over
0.95 at a tolerance of just 0.15m, thereby establishing a new benchmark.
Furthermore, corroborative real-world experiments and dataset analyzes mutually
validate each other, solidifying CurbNet's superior detection proficiency and
its robust generalizability.