# EnsembleVehicleDet: Detection of Faraway Vehicles with Real-Time Consideration
## 1 Introduction  
Autonomous driving is defined as driving with support from an autonomous driving system controlled by a computer without any external assistance. The possibility of accidents increases when this system cannot accurately detect objects such as vehicles. While improvements have been made to deep learning and deep learning-based object detection techniques [1; 2; 3; 4; 5; 6], there is still a problem where vehicles far from a camera are not detected accurately [7]. In this study, we denote small (or large) vehicles as faraway (or nearby) for the purpose of explanation because the faraway (or nearby) vehicles from a camera are shown as small (or large) objects in images captured from an autonomous driving system. Additionally, autonomous driving systems should be able to detect vehicles even when another vehicle from the opposite line approaches quickly; therefore, the execution speed [8] should also be considered when applying deep learning-based object detectors to autonomous driving systems. With many possible solutions to the accuracy problem, using the ensemble method [9; 10], studies have been conducted regarding improving the detection accuracy of vehicles [11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. Furthermore, many studies have reported the application of the You Only Look Once (YOLO) network, which is a single-stage Convolutional Neural Network (CNN) [21; 22; 23; 24; 25; 26; 27]that is superior in terms of detection accuracy and execution speed. Regarding this, some studies have reported [28; 29; 30] the use of the ensemble method on YOLO in a contest of object detection for autonomous driving. However, most of these studies have only focused on the improvement of accuracy or simply using fast detectors, neglecting to consider the tradeoff between detection accuracy and execution speed [31]. This study proposes an ensemble method that accurately detects small vehicles while satisfying the real-time requirement. In this method, the input image is downsampled, and the result of vehicular detection is acquired through the first object detector. Then, perspective transformation (i.e., warping) or upsampling is performed on the Region of Interest (RoI) where the small vehicles are located and applied on the second object detector. Finally, an ensemble method that uses two different object detectors is proposed. The downsampling of the input image does not degrade the detection accuracy of large vehicles. However, the second detector increases the pixel count of small vehicles in the transformed image beyond that in the input image, potentially improving the detection accuracy of small vehicles with faster execution speed. The accuracy results for the Argoverse dataset [32] divided by vehicle size are listed in Table 1. In this study, small, medium, and large vehicles are not arbitrarily chosen, rather, the 39,343 images of the Argoverse dataset are arranged in the order of vehicle sizes for the Ground Truth (GT) dataset and split into an even 33% ratio for each size, named small, medium, and large. AP\({}_{0.5}\) is the Average Precision (AP) value when the Intersection over Union (IoU) of the detection box and GT are more than 0.5, and thus, AP\({}_{0.5}\)-Small, AP\({}_{0.5}\)-Medium, and AP\({}_{0.5}\)-Large mean the accuracy of small, medium, and large vehicles, respectively. As shown in Table 1, the accuracy of small vehicles is relatively low compared to the accuracy of large vehicles; this difference in accuracy increases with a decrease in image resolution. This needs to be focused on to improve the accuracy of overall vehicle detection. Figure 1 shows the Ground Truth (GT) of the training images in the Argoverse dataset, which indicates the small vehicles in blue. As visualizing all of the Argoverse GT boxes of small vehicles with one image is difficult, the example shows the small vehicles only for the first 1000 images (shown as blue boxes) on the first Argoverse image. The example shows many small vehicles included in the RoI region.