# EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration
## 2 Background  
The goal of this study is vehicle detection, which is operated in real-time on an embedded board in an autonomous driving system. Many studies have reported the use of an ensemble method to improve vehicle detection accuracy. For example, some studies [11; 12] have reported accuracy improvement by using an ensemble method on SSD [33] and Faster-RCNN [34], YOLOv5 [25], and Faster-RCNN. A different study [13] reported an ensemble method with region-based fully convolutional neural networks R-FCN + YOLOv3,  
Figure 1: Illustration of small vehicle distribution for the first 1000 images of the Argoverse dataset. RFCN + RetinaNet, Cascade-RCNN + YOLOv3, and Cascade-RCNN [35] + RetinaNet. The results of an ensemble method were shown with residual networks ResNet-50 and SE-ResNeXt-50 [14]. Using the ensemble method, the detection accuracy has not only been improved in the vehicular environment but also in the field of unmanned aerial vehicles (UAVs) [15; 16; 17]; other studies have [18; 19; 20] also reported improvements in the detection accuracy using the concept of ensemble models. However, common aspects of these reported studies include the execution time not being reported or reductions in the execution speed after implementing the ensemble method. Therefore, other studies have reported on the application of the ensemble method using the one-stage CNN-based object detector YOLO [21; 22; 23; 24; 25; 26; 27], which considers the execution time of the autonomous driving system. For example, there have been studies [28; 29; 30] wherein real-time processing results have been obtained by applying the ensemble method to a YOLO-based object detector model on a V100 graphical processing unit (GPU). The results obtained using the proposed methods in previous studies are for expensive GPU environments; autonomous driving systems require processing in a low-cost GPU environment [31]. Previous studies in the last five years that have improved the accuracy of vehicle detection using the ensemble method are summarized in Table 2. The studies can be grouped as either using top-view, tilted-view, or side-view images. Additionally, many studies have solely focused on improving the accuracy, and as a result, have neglected to report the inference time and not specify whether the test was processed in a CPU/GPU environment. The current study utilized object size differences in side-view images, as in autonomous vehicles, instead of top/tilted-view and regions in which small vehicles appear. The proposed method also takes the execution speed on low-cost embedded boards into consideration. To summarize, an input image is downsampled, and then the detection result is acquired by applying a downsampled image to a fast object detector (i.e., first deep learning model A focusing on large vehicles). Additionally, the RoI, where small vehicles are present, undergoes perspective transformation or upsampling and is inputted into another fast object detector (i.e., second deep learning model B focusing on small vehicles). We propose an ensemble method that merges the results of these two detection models. Compressing the input image has almost no impact on the detection accuracy of large vehicles but has an impact on the detection accuracy of small vehicles. Increasing the number of pixels on a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Execution Time Per Image** & **Execution Speed** & **Reference** \\
**(CPU or GPU)** & **Improvement** & **Reference** \\ \hline
18,500 ms (not specified) & NO & [11] \\ \hline
22,300 ms (GTX GPU) & NO & [12] \\ \hline
131 ms (1080 GPU) & NO & [13] \\ \hline
1000 ms (TitanX GPU) & NO & [14] \\ \hline
1300 ms (not specified) & NO & [15] \\ \hline Not specified (not specified) & NO & [16] \\ \hline
1400 ms (CPU) & NO & [17] \\ \hline Not specified (not specified) & NO & [18] \\ \hline Not specified (not specified) & NO & [19] \\ \hline
1500 ms (TitanX GPU) & NO & [20] \\ \hline
11 ms (AGX Embedded GPU) & YES & Proposed Method \\ \hline \hline \end{tabular}
\end{table}