Roadside cameras effectively enhance the perception capabilities of embodied artificial intelligence systems such as vehicles by compensating for the limitations of vehicle-mounted cameras, which are prone to occlusion and have a limited sensing range, thereby improving the safety of autonomous vehicles. However, existing object detection systems often encounter perception errors when handling comprehensive viewpoint noise in roadside scenes, as well as variations in traffic flow, lighting conditions, and camera poses. This makes it challenging for them to perform robustly in complex road environments. To address these issues, we propose \ (R^2MOAG\), a highly robust monocular 3D object detection method for roadside systems, based on ground perception embedding and heterogeneous visual tokens. The proposed method extracts detailed road information through ground plane equations and utilizes heterogeneous visual tokens to focus on foreground features. By integrating low-dimensional ground information with high-dimensional visual features, the model is provided with clear and rich cues for object detection, significantly enhancing its stability. We conducted extensive experiments on the widely recognized roadside datasets DAIR-V2X-I and Rope3D. The results show that, in terms of overall performance, the proposed model achieved a 4. 65% and 4. 26% improvement in the \ (AP₃₃|ₑ₄₀\) metric for the vehicle category on these two datasets, respectively. Moreover, the model maintained stable recognition performance across various road scenarios and camera poses, demonstrating exceptional robustness.
Tang et al. (Mon,) studied this question.