## 4 Experimental Evaluation
### Evaluation Results  
In this subsection, we evaluate the performance of the proposed inference models combined with the YOLOv3, YOLOv4, and YOLOv7 algorithms, which can classify the alignment status of shipping containers and infer container accidents. We also compare six models: YOLOv3 with 416 pixels (Model_1), YOLOv3 with 640 pixels (Model_2), YOLOv4 with 416 pixels (Model_3), YOLOv4 with 640 pixels (Model_4), YOLOv7 with 416 pixels (Model_5), and YOLOv7 with 640 pixels (Model_6). Firstly, to evaluate the object detection accuracy, we used Equation (1), and the results of these experiments are presented in Table 9. The results demonstrate that the models with an image resolution value of 640 \(\times\) 640 \(\times\) 3 achieved better accuracy than the models with an image resolution value of 416 \(\times\) 416 \(\times\) 3. The average accuracy of Model_4 reached 92.5%, showing high accuracy compared with the other models. The results for the F1 score, precision, and recall for each inference model (IoU = 0.5) are shown in Figure 11 and Table 10. As shown in the results, for precision, model_4 performed better than the other models with a score of 92.5%. model_6 achieved a score of 70.5%, model_5 achieved a score of 64.8%, model_3 achieved a score of 90.1%, model_2 achieved a score of 81.7%, and model_1 achieved a score of 79.0%. For recall, model_4's best score was 92.5%, while model_6 achieved a score of 75.4%, model_5 achieved a score of 74.3%, model_3 achieved a score of 90.2%, model_2 achieved a score of 81.7%, and model_1 achieved a score of 79.0%. For the F1 score, model_4 performed better than the other models, with a score of 92.5%; model_6 achieved a score of 72.9%; model_5 achieved a score of 69.2%; model_3 achieved a score of 90.2%; model_2 achieved a score of 80.9%; and model_1 achieved a score of 78.2%. Based on these results, it is clear that model_4 outperformed the other models in all the performance metrics calculated and that the YOLOv7-based models were unable to recognize small objects efficiently. Table 11 shows the mean scores for precision, recall, and F1 score performance, with an IoU range from 0.5 to 0.9. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Model\_1** & **Model\_2** & **Model\_3** & **Model\_4** & **Model\_5** & **Model\_6** \\ \hline Precision & 79.0\% & 81.7\% & 90.1\% & 92.5\% & 64.8\% & 70.5\% \\ Recall & 79.6\% & 82.0\% & 90.2\% & 92.5\% & 74.3\% & 75.4\% \\ F1-score & 78.2\% & 80.9\% & 90.2\% & 92.5\% & 69.2\% & 72.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Precision, recall, and F1 score performance of each model (IoU = 0.5). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Model\_1** & **Model\_2** & **Model\_3** & **Model\_4** & **Model\_5** & **Model\_6** \\ \hline Momentum & 0.9 & 0.9 & 0.949 & 0.949 & 0.937 & 0.937 \\ Decay & 0.0005 & 0.0005 & 0.0005 & 0.0005 & 0.0005 & 0.0005 \\ Angle & 0 & 0 & 0 & 0 & 0 & 0 \\ Saturation & 1.5 & 1.5 & 1.5 & 1.5 & 0.7 & 0.7 \\ Exposure & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 & 1.5 \\ Hue & 0.1 & 0.1 & 0.1 & 0.1 & 0.015 & 0.015 \\ Learning & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ Rate & 0.1, 0.1 & 0.1, 0.1 & 0.1, 0.1 & 0.1, 0.1 & 0.9 & 0.9 \\ IoU & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Model\_1** & **Model\_2** & **Model\_3** & **Model\_4** & **Model\_5** & **Model\_6** \\ \hline Accuracy & 79.6\% & 82.0\% & 90.2\% & 92.5\% & 66.8\% & 73.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Object detection accuracy of each model. The recognition results for model_1 (Figure 12), model_2 (Figure 13), model_3 (Figure 14), and model_4 (Figure 15) are shown in Figures 12-15. model_4 had better recognition accuracy than model_1, model_2, and model_3. In each model's recognition result image, there are boxes and a central dot.