基于立体图像的多路径特征金字塔网络3D目标检测

3D目标检测是计算机视觉和自动驾驶中一项重要的场景理解任务。当前基于立体图像的3D目标检测方法大多没有充分考虑多个目标之间的尺度存在较大差异，从而尺度小的物体容易被忽略，导致检测精度低。针对这一问题，提出了一种基于立体图像的多路径特征金字塔网络(MpFPN)3D目标检测方法。MpFPN对特征金字塔网络进行了扩展，增加了自底向上的路径、由上至下的路径及输入特征图到输出特征图之间的连接，为联合区域提议网络提供了更高语义信息和更细粒度空间信息的多尺度特征信息。实验结果表明:在3D目标检测KITTI数据集上，无论在场景简单、中等、复杂情况下，所提方法获得的结果都优于比较方法的结果。 

3D object detection based on multi-path feature pyramid network for stereo images

3D object detection is an important scene understanding task in computer vision and autonomous driving. However, most of these methods do not fully consider the large differences in scales between multiple objects. Thus, objects with a small scale are easily ignored, resulting in low detection accuracy. To address this problem, this paper proposes a 3D object detection method based on multi-path feature pyramid network (MpFPN) for stereo images. MpFPN extends feature pyramid network, adding a bottom-up path, top-down path, and connections between input and output features. It provides multi-scale feature information with higher semantic information and finer-grained spatial information for union region proposal network. Experimental results show that the proposed method achieves better results than comparative methods in easy, moderate and hard scenarios on the 3D object detection dataset KITTI.