# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 1 Introduction  
Video surveillance systems are broadly used in many countries to monitor traffic volume and to detect accidents at an early stage to prevent secondary accidents [1; 2; 3]. Typically, these accidents involve vehicles or pedestrians on the road, making it essential to implement early object detection systems based on CCTVs. This requirement is mandatory in most countries. Based on the detected object information, it becomes possible to develop evaluation algorithms or accident response processes for CCTV surveillance systems. For this purpose, a number of studies have been conducted in various academic fields, such as emergency transportation methods [4], road vehicle accident responses [5], railway crossing surveillance [6], and monitoring systems of construction vehicles at construction sites [7; 8]. In tunnels, there is a much higher probability of road traffic accidents in entering or exiting zones because of illumination changes which sometimes cause temporary light or dark adaptations to drivers [9; 10; 11; 12]. Additionally, evacuation areas are often limited in the tunnels, and this may lead to severe secondary accidents. To minimize those accidents in such environments, it is crucial to promptly detect and respond to the accidents via video surveillance systems. CCTVs in the tunnels are normally installed at lower positions compared to those on open roads, as illustrated in Figure 1. Therefore, these CCTV installations lead to severely distorted perspective footage, and this image distortion causespoor recognition of vehicles or humans as the relative distance between the CCTVs and the identified objects increases [13]. In particular, this mechanism has disadvantages in object detection (OD) performance using computer vision techniques [14]. Figure 1 shows the captured images from two different CCTVs on a highway and a tunnel, demonstrating the effect of installation height on perspective and overlap. In an open road site, First, as shown in Figure 1a, the CCTV can be installed at a height of at least 8 m or higher, as there are no space constraints in the vertical direction. Consequently, the perspective effect is less pronounced, as in Figure 1a. In addition, the lane width is represented by white straight lines, at the end of a distance of about 100 m. The lane width at the far side appears to be approximately 0.5 times smaller than the one at the near side. On the other hand, in the tunnel CCTV (Figure 1b), the lane width at the far side appears to be 0.09 times smaller than the one at 100 m away, indicating a more severe perspective effect compared to Figure 1a [15]. In addition, there is a significant difference in the overlapping of vehicles depending on the CCTV installation height. The higher the CCTV installation height, the closer it is to the top-view image. Therefore, in situations with numerous vehicles in traffic (Figure 1a), only a portion of them overlap, allowing for a better distinction of each vehicle, as seen in the actual road image in Figure 1a. In contrast, as illustrated in Figure 1b, vehicles that are distinct from the CCTV appear to be heavily overlapped, which could potentially obstruct the visibility of rear vehicles. To address the issue of decreased OD performance due to perspective, [PERSON] et al. conducted a study aiming to improve the OD performance for detecting persons in tunnel CCTV images using high-resolution reconstruction [17]. [PERSON] et al. [17] reported a 2% improvement in the OD performance for detecting persons, increasing from 88% to 90%. However, [PERSON]. et al. [17] did not address any geometrical effect of the high perspective phenomenon which, we believe, might be more sensitive in OD performance than the resolution of CCTV in tunnel environment. In fact, OD performance in tunnel sites has not been addressed to date, even if a traffic accident in a tunnel causes highly risky secondary accidents with expected low OD performance in a highly disadvantageous tunnel CCTV environment.