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.

翻译：路缘检测是智能驾驶中的重要功能，可用于确定道路的可行驶区域。然而，由于复杂的道路环境，路缘难以检测。本文提出CurbNet，一种利用点云分割的新型路缘检测框架。针对路缘综合数据集匮乏及缺乏三维标注的问题，我们开发了3D-Curb数据集，包含7100帧，是当前规模最大、类别最丰富的路缘点云数据集。考虑到路缘主要由高度变化特征表征，我们的方法利用空间丰富的三维点云进行训练。为解决路缘特征在xy平面分布不均及其依赖z轴高频特征带来的挑战，我们引入多尺度通道注意力（MSCA）模块，这是一种专门优化检测性能的定制化解决方案。此外，我们提出自适应加权损失函数组，专门用于缓解路缘点云相对于其他类别分布不平衡的问题。在两个主要数据集上的大量实验结果表明，该方法超越了现有领先的路缘检测和点云分割模型所设定的基准。通过在后处理阶段融合多聚类与曲线拟合技术，我们显著降低了路缘检测中的噪声，将精度提升至0.8744。值得注意的是，CurbNet在仅0.15米的容差下实现了超过0.95的卓越平均指标，从而树立了新的基准。此外，相互印证的现实世界实验与数据集分析共同证实了CurbNet卓越的检测能力及其强大的泛化性。