# EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration
## 3 Proposed Method
### Obtaining Detection Results from Transformed Image
In addition, we applied a downsampled image to Model A, applied a transformed image to Model B, and then ensembled the detection results from A and B to further increase the accuracy-speed tradeoff. Overall, the vehicle detection accuracy-speed tradeoff was shown to improve from 23.32 to 36.28 for 1920 \(\times\) 1216 input images. Using an embedded GPU (AGX board) with TensorRT, however, the proposed method could improve the accuracy-speed integrated performance significantly from 36.34 to 68.24 for 960 \(\times\) 608 input images. While there is a limitation to using the proposed method for curves in an intersection, most vehicles are assumed to drive straight rather than curved, which shows that this is not such a large shortcoming; overall, the proposed method can still improve the accuracy. Future studies should focus on pedestrians that are smaller than vehicles in object detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & **Resolution 1** & **Resolution 2** & **AP\({}_{0.5}\)** & **FPS** & **AP\({}_{0.5}\)\(\times\) FPS** \\  & **(Model A)** & **(Model B)** & \(\uparrow\) & \(\uparrow\) & \(\uparrow\) \\ \hline Baseline & YOLOv7-Tiny & 960 \(\times\) 608 & 0.79 & 46.00 & 36.34 \\ \hline \multirow{3}{*}{Proposed Method} & Ensemble-Warping & 480 \(\times\) 320 & 960 \(\times\) 320 & 0.77 & 85.20 & 65.60 \\  & Ensemble-Upsampling \#1 & 480 \(\times\) 320 & 960 \(\times\) 320 & 0.80 & 85.30 & 68.24 \\  & Ensemble-Upsampling \#2 & 480 \(\times\) 320 & 1920 \(\times\) 320 & 0.79 & 62.10 & 49.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy–speed performance comparison using an Embedded GPU (AGX board) of 960 \(\times\) 608 input images (with TensorRT). EnsembleVehicleDet could improve the accuracy–speed tradeoff (i.e., AP\({}_{0.5}\)\(\times\) FPS) of the baseline from 36.34 to 68.24. **Author Contributions:** [PERSON] and [PERSON] conceptualized and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] designed and implemented the detection system; [PERSON] and [PERSON] validated the proposed method; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** This patent is the result of a local government-university cooperation-based regional innovation project (2021 RJS-004), carried out with the support of the Korea Research Foundation with the funding of the Ministry of Education in 2021. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. Figure 17: Comparison of Model A, Model B (Warping), and Ensemble-Warping results. With Ensemble-Warping, one FN error in the first image, two FN errors in the second image, and two FN errors in the third image were solved (shown as red circles), although one FP error was generated in the first image (shown as a brown circle). ## References

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