# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 4 Experiments
### Experiments and Analysis  
In the experiments, this paper primarily compared its approach with the following monocular visual 3D object detection algorithms: (1) KM3D, which employed ResNet34 as the backbone network. (2) Kinematic3D, a video-based monocular 3D object detector utilizing DenseNet121 as the backbone network. (3) MonoDLE, a center-point-based single-stage detector based on CenterNet, employing DLA34 as the backbone network.  
In the experimental setup, \"Ours Base\" represents the improved baseline model of YOLOv7 capable of simultaneously detecting 2D and 3D objects. \"Ours +5 Head\" indicates the model with five detection heads built upon the base model. \"Ours +MSSA\" signifies the YOLOv7-3D model incorporating the multi-scale spatial attention mechanism on top of the previous model.  
The comparison of the experimental and ablation study results is shown in Table 1. The analysis of experimental results reveals that KM3D achieved lower average precision across all categories. It performed relatively well in the Cyclist and Motorcyclist categories but poorly in the Bus and Truck categories. Kinematic3D, on the other hand, obtained higher average precision compared to KM3D across all categories. It performed well in the Cyclist, Motorcyclist, and Pedestrian categories but poorly in the Van and Bus categories. MonoDLE achieved higher average precision than KM3D and Kinematic3D across all categories. It performed well in the Cyclist, Motorcyclist, and Pedestrian categories. Ours Base showed poor performance in the Car, Van, and Bus categories but relatively good performance in the Cyclist, Motorcyclist, and Pedestrian categories. Adding five detection heads to the Base model resulted in higher average precision across all categories compared to Ours Base. It performed well in the Cyclist, Motorcyclist, and Pedestrian categories.  
Figure 9: Presentation of data augmentation results. We implemented random scaling, cropping, changes in color space, and brightness adjustments in the data augmentation algorithm to enhance the robustness of the model.  
Finally, incorporating MSSA resulted in the highest average precision across all categories. It performed best in the Cyclist, Motorcyclist, and Pedestrian categories. Overall, based on the given experimental data, the Ours + MSSA method achieved the best average precision across multiple categories. Additionally, categories such as Bus achieved lower precision compared to the aforementioned methods due to the imbalance in the dataset, where the Bus and Truck categories have a very small number of instances, resulting in less training exposure for the model to learn other categories effectively.  
The model's parameter count and computational load are crucial metrics for assessing algorithm detection speed. The results indicate that Ours Base has a relatively large parameter count compared to other models. However, by modifying it to incorporate five detection heads and halving the main channel count, the model's parameters increased from 37\(M\) to 42\(M\). Notably, the computational load decreased significantly from 222G to 87G, accompanied by improvements in the supervised accuracy for various categories. In contrast, Ours + MSSA showed only a slight increase in parameter count yet achieved enhanced detection accuracy for different target categories. This suggests that Ours + MSSA improves model performance without introducing a substantial number of redundant parameters and computational burden. Considering accuracy, model parameter count, and computational load comprehensively, Ours + MSSA demonstrates outstanding performance in traffic object detection tasks. The method enhances detection accuracy while maintaining a relatively modest model complexity, showcasing excellent practicality.