# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
## 5 Experiments and Result Analysis
### Comparison of Different Feature Fusion Methods  
To validate the effect of feature fusion strategies, we compared five models, namely CentrNet, CBNet-CentrNet, BiFPN-CentrNet, RFP-CentrNet, and RCBi-CenterNet, in which CBNet-CentrNet employs the dual-backbone feature extractor, BiFPN-CentrNet adds a BiFPN block into CenterNet, and RFP-CenterNet adds feedback connections to CenterNet and enables a two-step sequential network. All five models were evaluated on the same dataset and used the same backbone. Note that CenterNet is regarded as the SOTA as it was one of the models that won the Gold medal of the competition ([[https://www.kaggle.com/diegojhnson/centernet-objects-as-points](https://www.kaggle.com/diegojhnson/centernet-objects-as-points)]([https://www.kaggle.com/diegojhnson/centernet-objects-as-points](https://www.kaggle.com/diegojhnson/centernet-objects-as-points)), accessed on 20 May 2021). The results are shown in Figure 9. We provide our observations and insights as follows.  
* Adding a composite dual-backbone network to CenterNet alone was not effective, reducing the mAP by 1-2%. However, we kept the dual-backbone component because once we removed a backbone from RCBi-CenterNet, a performance drop of 2-4% in all three metrics was observed, meaning that the dual-backbone design worked better with BiFPN and the feedback connections integrated into the same system.
* The addition of BiFPN boosted Top 3 and Top 10 by 0.9% and 2.37%, respectively, and decreasesd Top 1 by 9.4%, meaning that the BiFPN module could help classify more objects with a looser threshold, but its performance in Top 1 was greatly reduced compared to CenterNet.
* The implementation of a recursive network brought down both Top 1 and Top 3 by 3-4%, but boosted Top 10 by 3.4%, presenting a similar effect with BiFPN.
* Combining the dual-backbone and BiFPN modules in a recursive fashion, the resulting RCBi-CenterNet model showed superior performance over CenterNet, with 2.16%, 2.76%, and 5.24% performance gains in Top 1, Top 3, and Top 10, respectively. This result demonstrates that the proposed network architecture can effectively extract, fuse, and represent distinguishable features for object detection in the domain of autonomous driving.  
Figure 8: Effects of different backbones used in the dual-backbone feature extractor.  
In addition, we provide the inference speed for the five evaluated models in Figure 10. As expected, the inference speed dropped from 23.8 frames per second (FPS) to 13.2 FPS, as we added more feature fusion strategies to the base CenterNet model. Despite the speed degradation, our RCBi_CenterNet can still meet the requirement of real-time inference for practical use. In [56], the authors presented a YOLO-based real-time object detector that runs 13 FPS on the Microsoft HoloLens. [PERSON] et al. proposed a CenterNet-based model with weighted feature fusion and attention mechanism [57] for object detection and had a speed of 13.5 FPS on four NVIDIA TITAN Xp GPUs. In [58], a mobile CenterNet was developed and achieved a speed of 7 FPS on an NVIDIA TX2. These studies show that the speed of 13.2 FPS for our model is practical. Meanwhile, we realize that there is always a trade-off between accuracy and speed, where speed can be improved with better hardware, but accuracy can only be improved with better architecture design.