Camera-Radar-Based Adaptive Cross Attention Fusion for Multimodal Object Detection

Advanced driver assistance systems (ADAS) necessitate the integration of data from multiple sensors for accurate environmental perception and object detection. However, fusing information from diverse sensors like cameras and radar presents substantial challenges due to inherent differences in data formats and the need for sophisticated fusion strategies. This paper introduces an Adaptive Cross Attention Fusion Module (ACAFM) tailored for the effective fusion of image and millimeter-wave radar data. By leveraging both channel and spatial attention mechanisms, our approach dynamically adjusts feature weighting during the fusion process, significantly improving detection accuracy and system stability. Evaluations on public datasets demonstrate that our method outperforms existing camera-radar fusion techniques, highlighting its robustness in complex and varied scenarios.