## 3 Proposed Method  
Dataset: This study used datasets from 'The Open AI Dataset Project (Multi-Video Same Situation and Object Identification Data)'. All data information can be accessed through AI-Hub (www.aihub.or.kr (accessed on 27 February 2024)). Figures 1-3 represent images captured from CCTV, a dashcam, and a smartphone, respectively, in the dataset. This dataset was collected using CCTV, dashcam, and smartphone devices in the same scenarios, with 12 images captured for each device in each scenario. Currently, it is in a limited beta version with restricted public access, containing incidents related specifically to collisions between humans and bicycles. The images are provided in full HD (1920 \(\times\) 1080) resolution. As the data were collected in identical scenarios, the frame rate is consistent, ensuring a uniform number of images across all device types. However, some slight inconsistencies may exist in publicly available datasets due to issues such as omissions and errors. The object detection classes include (1) person, (2) scooter, (3) vehicle, and (4) bicycle. However, the scooter class is currently not included in the publicly available dataset. These three types of data can be denoted as CCTV (CT), dashcam (black box--BB), and smartphone (SP), respectively. After a brief examination of the dataset to identify device-specific characteristics, it was observed that the CCTV footage appears to be captured from an overhead perspective, resembling actual CCTV footage. The dashcam footage exhibits darkness due to vehicle tinting, indicating an interior view from within the vehicle. In contrast, the smartphone footage shares a similar screen angle with dashcam footage but comparatively presents a cleaner screen. The total dataset consists of 3811 images for CCTV, 3816 images for the dashcam, and 3804 images for the smartphone, showing a slight inconsistency in the total number of images for each device. Overall, there are 11,431 image-label pairs in the dataset. The dataset was manually split into training, validation, and test sets. According to Table 1, the training set is divided into CCTV (2088 images), black box (2088 images), and smartphone (2087 images) for a total of 6263 images; the validation set into CCTV (576 images), black box (576 images), and smartphone (576 images), for a total of 1728 images; and the test set into CCTV (1147 images), black box (1152 images), and smartphone (1141 images), for a total of 3440 images. Training the YOLO [26] model: To train the YOLOv8 object detection model, text files with the same names as the image files are required. These text files must contain the class number of the object and bounding box information. The bounding box format for the YOLO model requires normalized values in the order of the center coordinates (x, y), width (w), and height (h) as [x, y, w, h]. Annotation information for the publicly available dataset is in the JSON file format, containing various pieces of detailed information that can be utilized for different research purposes. However, for this experiment, many unnecessary details were present, and the bounding box format did not match the YOLO bounding box format. Therefore, as shown in Figure 4, a conversion was performed. Figure 3: Dataset example (smartphone). Utilizing auto-labeling with Grounding DINO [29]: Although the YOLO model boasts excellent speed and parameter efficiency, its detection performance on this dataset did not meet expectations. Considerations for performance improvement included altering the model structure, employing more effective transfer learning, or enhancing label quality. Since the YOLO model is well-developed and transfer learning has been effectively conducted on the COCO dataset [31], making changes to it posed difficulties. Therefore, the primary approach focused on enhancing the label quality of this dataset, anticipating that it would contribute to performance improvement. Recently, the zero-shot object detection model, Grounding DINO, has demonstrated outstanding detection performance.