基于多视角融合的夜间无人车三维目标检测

为了提高无人车在夜间情况下对周围环境的物体识别能力，提出一种基于多视角通道融合网络的无人车夜间三维目标检测方法。引入多传感器融合的思想，在红外图像的基础上加入激光雷达点云进行目标检测。通过对激光雷达点云进行编码变换成鸟瞰图形式和前视图形式，与红外图像组成多视角通道，各通道信息之间融合互补，从而提高夜间无人车对周围物体的识别能力。该网络将红外图像与激光雷达点云作为网络的输入，网络通过特征提取层、候选区域层和通道融合层准确地回归检测出目标的位置以及所属的类别。实验结果表明，该方法能够提高无人车在夜间的物体识别能力，在实验室的测试数据中准确率达到90%，速度0.43 s/帧，达到了实际应用要求。

Nighttime three-dimensional target detection of driverless vehicles based on multi-view channel fusion network

In order to improve the ability of driverless vehicles to identify objects in the surrounding environment at night,a three-dimensional target detection method for driverless vehicles based on multi-view channel fusion network was proposed.The idea of multi-sensor fusion was adopted,and the target detection was carried out by adding laser radar point cloud on the basis of infrared image.By encoding the laser radar point cloud into a bird’s-eye view form and a front view form,as well as forming a multi-view channel with the infrared night vision image,the information of each channel was fused and complemented,thereby improving the ability of the driverless vehicle to recognize surrounding objects at night.The infrared image and the laser radar point cloud were used as the input of the network.The network accurately detected the position of the target and the category by the feature extraction layer,the regional suggestion layer and the channel fusion layer.The method can improve the object recognition ability of driverless vehicles at night,the accuracy rate in the laboratory test data reaches 90%,and the time consumption of 0.43 fps also basically meets the practical application requirements.