## 5 Conclusions and Future Works  
In this study, it was confirmed that there is no degradation in object detection performance when utilizing automatically generated labels. Furthermore, combining auto-labels with manual labels resulted in an enhancement of object detection performance. These  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Group** & **Test Group** & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\  & **mAP50** & **mAP50** & **mAP50** & **mAP50** \\ \hline Group 1 (CT, BB, SP) & 0.644 & 0.627 & 0.639 & 0.676 \\ Group 2 (CT) & 0.473 & 0.597 & 0.374 & 0.423 \\ Group 3 (BB) & 0.458 & 0.345 & 0.495 & 0.555 \\ Group 4 (SP) & 0.53 & 0.322 & 0.578 & 0.732 \\ Group 5 (CT, BB) & 0.567 & 0.573 & 0.536 & 0.593 \\ Group 6 (CT, SP) & 0.609 & 0.579 & 0.565 & 0.697 \\ Group 7 (BB, SP) & 0.585 & 0.477 & 0.597 & 0.732 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Training results using auto-labels generated by Grounding DINO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training Group** & **Test Group** & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\  & **mAP50** & **mAP50** & **mAP50** & **mAP50** \\ \hline Group 1 (CT, BB, SP) & 0.652 & 0.608 & 0.635 & 0.757 \\ Group 2 (CT) & 0.435 & 0.567 & 0.363 & 0.339 \\ Group 3 (BB) & 0.465 & 0.365 & 0.485 & 0.607 \\ Group 4 (SP) & 0.534 & 0.375 & 0.53 & 0.757 \\ Group 5 (CT, BB) & 0.611 & 0.628 & 0.539 & 0.7 \\ Group 6 (CT, SP) & 0.664 & 0.647 & 0.624 & 0.76 \\ Group 7 (BB, SP) & 0.569 & 0.437 & 0.58 & 0.771 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Combined training results of auto-labels and manual labels.  
results are applicable only when objects can be detected by Grounding DINO. Additionally, experiments and analyses were conducted using a dataset that includes data collected from various devices such as CCTV, dashcams, and smartphones. This aimed to investigate the impact of each device type on the accuracy of object detection. The auto-labeling technology of Grounding DINO demonstrated its efficiency and performance improvement in the field of object detection, providing evidence for its practical applicability.  
Future research should focus on the integration and effective utilization of images obtained from various devices, as they offer broader perspectives and rich information. Additionally, beyond simple combination, there is a need to explore more synergistic and efficient methods for integrating auto-labels and manual labels.  
J.S.: conceptualization, methodology, software, investigation, data curation, writing--original draft preparation, writing--original draft preparation, visualization. H.J.: conceptualization, project administration, supervision, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2024-2020-0-01808) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. RS-2023-00241123).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.aihib.ox.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=71467](https://www.aihib.ox.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=71467)]([https://www.aihib.ox.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=71467](https://www.aihib.ox.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=71467)) (accessed on 27 February 2024).  
The authors declare no conflict of interest.