基于注意力的毫米波-激光雷达融合目标检测

针对自动驾驶中使用激光雷达进行目标检测时漏检被遮挡目标、远距离目标和复杂天气场景下目标的问题，提出一种基于注意力机制的毫米波-激光雷达特征融合的目标检测方法。首先，将毫米波和激光雷达各自的扫描帧数据分别聚合到它们的标注帧上，并将毫米波和激光雷达的数据点进行空间对齐；其次，对两者进行聚合和空间对齐后的数据分别进行PointPillar点云柱快速编码，转换成伪图像；最后，通过中间卷积层提取两者的传感器特征，并利用注意力机制对两者的特征图进行融合，融合后的特征图通过单阶段检测器得到检测结果。实验结果显示，该融合算法在nuScenes数据集中的平均精度均值（mAP）高于PointPillar基础网络，而且注意力融合的检测方法的性能表现优于利用拼接融合、相乘融合、相加融合的检测方法。可视化结果显示所提方法是有效的，能提高网络对被遮挡目标、远处目标和雨雾天气下目标检测的鲁棒性。

Attention-based object detection with millimeter wave radar-lidar fusion

To address problems of missing occluded objects, distant objects and objects in extreme weather scenarios when using lidar for object detection in autonomous driving, an attention-based object detection method with millimeter wave radar-lidar feature fusion was proposed. Firstly, the scan frame data of millimeter wave radar and lidar were aggregated into their respective labeled frames, and the points of millimeter wave radar and lidar were spatially aligned, then PointPillar was employed to encode both the millimeter wave radar and lidar data into pseudo images. Finally, the features of both millimeter wave radar and lidar sensors were extracted by the middle convolution layer, and the features maps of them were fused by attention mechanism, and the fused feature map was passed through a single-stage detector to obtain detection results. Experimental results on nuScenes dataset show that compared to the basic PointPillar network, the mean Average Precision (mAP) of the proposed attention fusion algorithm is higher, which performs better than concatenation fusion, multiply fusion and add fusion methods. The visualization results show that the proposed method is effective and can improve the robustness of the network for detecting occluded objects, distant objects and objects surrounded by rain and fog.