Intersection Is Also Needed: A Novel LiDAR-Based Road Intersection Dataset and Detection Method

3D object detection is crucial for autonomous driving. However, most existing methods focus on the foreground objects, such as vehicles and pedestrians, while ignoring some important background objects for traffic scene understanding, especially road intersections. Moreover, existing datasets (e.g., KITTI, Waymo) do not provide the labels for intersections, and the evaluation metric is also unsuitable for intersection detection. To address the above issues, we first present a LiDAR-based intersection dataset on the basis of KITTI dataset, called
KITTI-Intersection Dataset
. The new dataset includes 4718 frames with 5178 instances belonging to Forkroad and Crossroad, respectively. To weaken the impact of uncertain intersection size on the performance evaluation, we introduce CEIOU instead of IOU as a new evaluation metric. Then, we propose
MInsectDet
and
MMInsectDet
, two LiDAR-based detection methods, to solve the intersection detection problem. We start with a lightweight BEV backbone to alleviate the influence of numerous dynamic foreground objects at the intersection and obtain discriminative features. After that, to obtain more abundant and complete intersection features, we propose a Multi-Representation Backbone that integrates the BEV and voxel features to achieve better detection performance. Furthermore, in order to better adapt to various appearances and sizes of intersection, we propose a Class-Aware MultiHead, which classifies and regresses different categories with specific head. Finally, we evaluate our MInsectDet and MMInsectDet methods on the proposed KITTI-Intersection Dataset with the state-of-the-art foreground 3D detection methods. The results show that MMInsectDet achieves the best performance, and MInsectDet ranks second but could run at 65.0 FPS.