A Hybrid Model for Object Detection Based on Feature-Level Camera-Radar Fusion in Autonomous Driving

Preventing car collisions through object detection has always been a major research direction in the field of autonomous driving. Recent years, camera-based object detection technology has achieved great success. However, its performance is still insufficient under poor lighting or weather conditions. Therefore, the fusion of various sensor information has become a new trend in object detection for autonomous driving. This paper proposes a hybrid object detection model that fuses millimeter-wave radar and camera at the feature level. The model uses a traditional convolutional neural network to extract features from data collected by the radar and camera, and performs multi-scale deep fusion. Subsequently, a multi-scale deformable attention module is used to process the fused feature maps for object detection. We tested this model on the nuScenes for autonomous driving, which includes night and rainy scenes. The hybrid model achieved a mean average precision (mAP) of 47.8%, which is 1.4% higher than that of the baseline object detection model.