RGB Image- and Lidar-Based 3D Object Detection Under Multiple Lighting Scenarios
RGB Image- and Lidar-Based 3D Object Detection Under Multiple Lighting Scenarios
复制标题
DOI：
10.1007/s42154-022-00176-2
复制
发表时间：
2022-04
影响因子：
6.1
通讯作者：
Wen‐huan Chen;Wei Tian;Xiang-Wen Xie;Wilhelm Stork
中科院分区：
文献类型：
--
作者：
Wen‐huan Chen;Wei Tian;Xiang-Wen Xie;Wilhelm Stork
近年来，基于相机和激光雷达的3D目标检测取得了很大的进展。然而，相关的研究主要集中在正常光照条件下，其3D检测算法的性能会下降，在低光照场景下，如在夜间。本文旨在提高多光照条件下三维车辆检测精度的融合策略。首先，在数据预处理过程中，结合距离和不确定性信息来指导语义信息在点云上的“绘画”。此外，设计了一个多任务框架，它结合了不确定性学习，以提高低光照场景下的检测精度。在KITTI和Dark-KITTI基准上的验证中，该方法将KITTI基准上的车辆检测准确率提高了1.35%，并且在建议的Dark-KITTI数据集上验证了模型的通用性，车辆检测的增益为0.64%。
In recent years, camera- and lidar-based 3D object detection has achieved great progress. However, the related researches mainly focus on normal illumination conditions; the performance of their 3D detection algorithms will decrease under low lighting scenarios such as in the night. This work attempts to improve the fusion strategies on 3D vehicle detection accuracy in multiple lighting conditions. First, distance and uncertainty information is incorporated to guide the “painting” of semantic information onto point cloud during the data preprocessing. Moreover, a multitask framework is designed, which incorporates uncertainty learning to improve detection accuracy under low-illumination scenarios. In the validation on KITTI and Dark-KITTI benchmark, the proposed method increases the vehicle detection accuracy on the KITTI benchmark by 1.35% and the generality of the model is validated on the proposed Dark-KITTI dataset, with a gain of 0.64% for vehicle detection.