基于激光点云与图像融合的3D目标检测研究

Research on 3D Object Detection Based on Laser Point Cloud and Image Fusion

目前基于激光雷达与摄像头融合的目标检测技术受到了广泛的关注,然而大部分融合算法难以精确检测行人、骑行人等较小目标物体,因此提出一种基于自注意力机制的点云特征融合网络。首先,改进Faster-RCNN目标检测网络以形成候选框,然后根据激光雷达和相机的投影关系提取出图像目标框中的视锥点云,减小点云的计算规模与空间搜索范围;其次,提出一种基于自注意力机制的Self-Attention PointNet网络结构,在视锥范围内对原始点云数据进行实例分割;然后,利用边界框回归PointNet网络和轻量级T-Net网络来预测目标点云的3D边界框参数,同时在损失函数中添加正则化项以提高检测精度;最后,在KITTI数据集上进行验证。结果表明,所提方法明显优于广泛应用的F-Point Net,在简单、中等和困难任务下,汽车、行人和骑行人的检测精度均得到较大的提升,其中骑行人的检测精度提升最为明显。同时,与许多主流的三维目标检测网络相比具有更高的准确率,有效地提高了3D目标检测的精度。

At present,3D object detection based on the fusion of lidar and camera has received extensive attention.However,most fusion algorithms are difficult to accurately detect small target objects such as pedestrians and cyclists.Therefore,a feature fusion network based on the self-attention mechanism is proposed,which fully considers the local feature information to achieve accurate 3D object detection.Firstly,to reduce the spatial search range of the point cloud,the Faster-RCNN is improved to form a candidate box.Then,the frustum point cloud was extracted according to the projection relationship between the lidar and the camera.Secondly,a Self-Attention PointNet based on the self-attention mechanism is proposed to segment the original point cloud data within the scope of the frustum.Finally,while using the PointNet and T-Net to predict the 3D bounding box parameters,the regularization term is considered in the loss function to achieve higher convergence accuracy.The KITTI dataset is used for verification and testing.The results show that this method is obviously superior to F-PointNet and the detection accuracy of cars,pedestrians,and cyclists has been greatly improved,and it has higher accuracy than mainstream 3D object detection networks.