As the roadside perception plays an increasingly significant role in the Connected Automated Vehicle Highway(CAVH) technologies, there are immediate needs of challenging real-world roadside datasets for bench marking and training various computer vision tasks such as 2D/3D object detection and multi-sensor fusion. In this paper, we firstly introduce a challenging BAAI-VANJEE roadside dataset which consist of LiDAR data and RGB images collected by VANJEE smart base station placed on the roadside about 4.5m high. This dataset contains 2500 frames of LiDAR data, 5000 frames of RGB images, including 20% collected at the same time. It also contains 12 classes of objects, 74K 3D object annotations and 105K 2D object annotations. By providing a real complex urban intersections and highway scenes, we expect the BAAI-VANJEE roadside dataset will actively assist the academic and industrial circles to accelerate the innovation research and achievement transformation in the field of intelligent transportation in big data era.
翻译:由于路边感知在连通自动车辆高速公路(CAVH)技术中发挥日益重要的作用,因此迫切需要具有挑战性的实际路边数据集,用于轮椅标识和培训各种计算机视觉任务,如2D/3D天体探测和多传感器聚合。在本文中,我们首先推出一个具有挑战性的BAAI-VANJEE路边数据集,由位于路边约4.5米高处的VANJEE智能基站收集的LIDAR数据和RGB图像组成。该数据集包含2500个LIDAR数据框架,5000个RGB图像框架,包括20%同时收集。它还包括12类物体,74K 3D天体说明和105K 2D天体说明。通过提供真正的复杂的城市交叉点和高速公路场景,我们期望BAAI-VANJEE路边数据集将积极协助学术界和产业界加快大数据时代智能运输领域的创新研究并实现转变。