Object detection in camera images, using deep learning has been proven
successfully in recent years. Rising detection rates and computationally
efficient network structures are pushing this technique towards application in
production vehicles. Nevertheless, the sensor quality of the camera is limited
in severe weather conditions and through increased sensor noise in sparsely lit
areas and at night. Our approach enhances current 2D object detection networks
by fusing camera data and projected sparse radar data in the network layers.
The proposed CameraRadarFusionNet (CRF-Net) automatically learns at which level
the fusion of the sensor data is most beneficial for the detection result.
Additionally, we introduce BlackIn, a training strategy inspired by Dropout,
which focuses the learning on a specific sensor type. We show that the fusion
network is able to outperform a state-of-the-art image-only network for two
different datasets. The code for this research will be made available to the
public at: this https URL

本文提出了一种通过融合镜头和雷达数据的 CameraRadarFusionNet 网络，在二维目标检测技术上取得了优于目前最新图像网络的性能，同时使用了专注于某一特定传感器类型的普遍训练策略 BlackIn。