# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 5 Conclusions  
This paper introduces an IPT, aiming to mitigate the pronounced perspective effect inherent in tunnel CCTVs. To ascertain the viability of this technique, both OI and TI were generated from tunnel CCTV footage taken at a simulated virtual tunnel site, all within the same region of interest (ROI). Subsequently, through a preliminary comparison of AV and vehicle size in relation to the actual position of vehicles moving at a consistent speed, TI demonstrated the capacity to mitigate the influence of velocity and size alterations caused by perspective effect. Specifically, as vehicles maintained a constant velocity, AV experienced a noticeable decline as they moved away from the tunnel CCTV in OI, whereas in TI, AV remained comparatively closer to the constant velocity. Expanding on this, deep learning datasets were established for both OI and TI to train the deep learning model under identical conditions. The OD performance of this model was then assessed across four distance intervals spanning 50 m to 200 m, noting that 200-250 m is a standard installation distance interval for tunnel CCTVs in South Korea. From this comprehensive investigation, a number of key conclusions can be drawn:  
1. In the tunnel, the CCTV installation distance interval is less than 100 m, and the utilization of both OI and TI yields acceptable OD performance within tunnels. Consequently, existing tunnel CCTV-based accident detection systems predominantly reliant on OI may encounter considerable difficulties not only in OD but also in unforeseen accident detection as well. 2. Conversely, when the spacing between tunnel CCTV installations exceeds 100 m, utilization of TI becomes advantageous. OI-based OD performance does not exhibit reliable performance beyond 100 m from the CCTV installation position. This implies that tunnel sites with CCTV installation intervals of around 200 m, adhering to Korean regulations, can expect acceptable automatic accident detection performance through TI without necessitating additional CCTV installation. 3. In TI, while the size of vehicle objects remains consistent across distances within tunnels, distant objects inherently experience more substantial degradation in sharpness compared to nearby objects. Nevertheless, OD performance analysis utilizing AP metric demonstrates distance-consistent performance in TI, suggesting that the impact of object size versus sharpness on OD performance is negligible. 4. When identifying the direction and velocity of vehicle movement to detect unexpected accidents like abrupt stops and backward travel of vehicles on tunnel roads, ensuring consistent AV evaluation from CCTV images proves notably beneficial because that phenomenon assists the object tracking of driving vehicles [52]. Nevertheless, a significant discord between AV and actual velocity in OI necessitates intricate algorithmic corrections that account for vehicle distance to prevent false detections. Consequently, a TI-based tunnel CCTV-based accident detection system could offer simplified accident detection implementation with more consistent and stable performance. In future studies, the effectiveness of TI through IPT will be re-evaluated in real tunnel sites, which introduce more complex backgrounds and a wider range of AV variations, as well as potential vehicle overlapping phenomena. Additional results will be reported in due course. Conceptualization, K.B.L., J.H.G., B.H.R. and H.S.S. ; Data curation, K.B.L. ; Formal analysis, J.H.G. and H.S.S. ; Funding acquisition, H.S.S. ; Investigation, K.B.L. and B.H.R. ; Methodology, K.B.L. and H.S.S. ; Project administration, H.S.S. ; Resources, K.B.L. ; Software, K.B.L. ; Supervision, H.S.S. ; Validation, H.S.S. ; Visualization, J.H.G. and H.S.S. ; Writing--original draft, K.B.L. ; Writing--review and editing, J.H.G. and H.S.S. All authors have read and agreed to the published version of the manuscript.