ISETAuto: Detecting vehicles with depth and radiance information

ISETAuto: Detecting vehicles with depth and radiance information

Autonomous driving applications use two types of sensor systems to identify vehicles - depth sensing LiDAR and radiance sensing cameras. We compare the performance (average precision) of a ResNet for vehicle detection in complex, daytime, driving scenes when the input is a depth map (D = d(x,y)), a radiance image (L = r(x,y)), or both [D,L].(1) When the spatial sampling resolution of the depth map and radiance image are equal to typical camera resolutions, a ResNet detects vehicles at higher average precision from depth than radiance. (2) As the spatial sampling of the depth map declines to the range of current LiDAR devices, the ResNet average precision is higher for radiance than depth. (3) For a hybrid system that combines a depth map and radiance image, the average precision is higher than using depth or radiance alone. We established these observations in simulation and then confirmed them using realworld data. The advantage of combining depth and radiance can be explained by noting that the two type of information have complementary weaknesses. The radiance data are limited by dynamic range and motion blur. The LiDAR data have relatively low spatial resolution. The ResNet combines the two data sources effectively to improve overall vehicle detection.

ISETAuto：使用深度和辐射率信息检测车辆

自动驾驶应用程序使用两种类型的传感器系统来识别车辆-深度感应LiDAR和辐射感测摄像头。当输入的是深度图（D = d（x，y）），辐射度图像（L = r（x，y）时，我们比较ResNet在复杂，白天，驾驶场景中用于车辆检测的性能（平均精度） ）），或同时使用[D，L]。.. （1）当深度图和辐射度图像的空间采样分辨率等于典型的相机分辨率时，ResNet从深度检测到的车辆平均精度要高于辐射度。（2）随着深度图的空间采样下降到当前LiDAR设备的范围，ResNet的辐射平均精度高于深度。（3）对于结合了深度图和辐射度图像的混合系统，平均精度要比单独使用深度或辐射度要高。我们在仿真中建立了这些观测值，然后使用真实世界的数据进行了确认。可以通过指出两种信息具有互补的弱点来解释结合深度和辐射度的优势。辐射数据受动态范围和运动模糊的限制。LiDAR数据的空间分辨率较低。 （阅读更多）