A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
This addresses the problem of unreliable camera-based detection in severe weather and low-light conditions for autonomous driving, representing an incremental improvement over existing methods.
The paper tackles object detection in autonomous vehicles by fusing camera and radar data to improve performance in adverse conditions, showing that their CameraRadarFusionNet outperforms a state-of-the-art image-only network on two datasets.
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: https://github.com/TUMFTM/CameraRadarFusionNet.