## 4 Experiments and Results  
### Dataset Group Splitting  
To investigate the impact of cross-device training on testing, the training and validation data were divided into seven groups (Tables 2 and 3: group 1 trained on CT, BB, and SP  
Figure 5: Teacher-student model using Grounding DINO and YOLO. Figure 6: Example of combining manual labels and auto-labels. Figure 9 illustrates the results of predicting bounding boxes for batches of training data, visually confirming the detection of all objects without any omissions. Figure 8: Confusion matrix for object detection training with manual labels for group 1. Figure 7: Training results for object detection using manual labels for group 1. Figure 9: Object detection prediction results on training data. Up to this point, we have examined performance metrics during training. Starting from the next section, the focus shifts to the results on the test dataset. Additionally, experiments were conducted by dividing the data into seven training groups and four testing groups. Metrics such as precision, recall, mAP50, and mAP50-95 were measured. To facilitate comparison across different groups, emphasis is primarily placed on mAP50, a performance metric commonly considered crucial. While Table 3 represents the division of the validation group, it is predominantly expressed as the training group in the context of result analysis. Table 5 presents the test results for manual label-based training across different groups. Upon examining the results, it is observed that dashcam (black box--BB) and smartphone (SP) exhibit similar trends, possibly due to the similarity in their screen displays. However, the overall performance of the dashcam is consistently lower than that of the smartphone, possibly attributed to the tinted nature of dashcam screens. Additionally, when looking at test group 2, a decline in performance is noted compared to training groups 1, 5, and 6, despite having a substantial dataset. This decline is attributed to the fact that training with data from different devices, where the angles of CCTV footage differ, leads to a decrease in performance. Examining training group 3, the overall performance is the lowest, suggesting potential issues with the quality of dashcam data or difficulties in identification. The subsequent experiment was conducted without utilizing a pre-trained model. Although it is generally acknowledged that fine-tuning a model pre-trained on a large dataset yields superior performance, there is a possibility of performance degradation due to differences in data domains or issues like overfitting. The results can be observed in Table 6, and in all experiments, the performance was consistently lower than the results obtained using a pre-trained model, as shown in Table 5. To assess whether the insufficient amount of training data was a contributing factor, we conducted training for group 1 up to 200 epochs. The results of training up to 200 epochs were superior to those of training up to 100 epochs, but it was evident that they were significantly inferior to the model trained for 100 epochs with pre-trained weights. From this experiment, we conclude that fine-tuning with a pre-trained model leads to superior performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Group** & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\
**Training Group** & **mAP50** & **mAP50** & **mAP50** & **mAP50** \\ \hline Group 1 (CT, BB, SP)100 epoch & 0.467 & 0.474 & 0.402 & 0.536 \\ Group 1 (CT, BB, SP)200 epoch & 0.508 & 0.496 & 0.499 & 0.537 \\ Group 2 (CT) & 0.276 & 0.393 & 0.187 & 0.287 \\ Group 3 (BB) & 0.117 & 0.0391 & 0.123 & 0.234 \\ Group 4 (SP) & 0.264 & 0.194 & 0.263 & 0.389 \\ Group 5 (CT, BB) & 0.419 & 0.477 & 0.345 & 0.397 \\ Group 6 (CT, SP) & 0.476 & 0.464 & 0.42 & 0.578 \\ Group 7 (BB, SP) & 0.302 & 0.2 & 0.312 & 0.433 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of manual labeling training without pre-trained models.