## 4 Experimental Results and Analysis
### Analysis of Generalization Performance Experiment Results
#### 4.4.2 Analysis of Experimental Results of Different Models  
To further validate the effectiveness of the optimized model, this study employed transfer learning to train popular object detection algorithms, for example, YOLOv3 [26], YOLOv5, and YOLOv7 [27], on the dataset. Additionally, a comparison was made with several representative lightweight convolutional network models, including Mobilenetv3 [28], EfficientNet [29], MobileNext [30], PP-LCNet [31], GhostNetV2 [32], and FasterNet [33]. We did not opt for object detection methods utilizing the two-stage approach from the R-CNN series, primarily because of the demanding need for real-time detection while maintaining a specific level of accuracy in the coal block field deployment. Achieving this level of performance is challenging with our current hardware infrastructure. For result reliability, all experiments were conducted in triplicate using distinct random seeds, and the average values were documented. The outcomes are displayed in Table 8 and Figure 15.  
Figure 14: Foreign object detection results in different environments. (**a–c**) Mist and dust recognition results; (**d–f**) low light recognition results; (**g–i**) motion blur recognition results. By comparison, it can be observed that among the YOLO series, YOLOv5x achieves the highest recognition accuracy. However, its 86.7M parameters and 205.7G FLOPs pose deployment challenges and demand high hardware device requirements, making it unsuitable for real-time detection tasks in underground coal mines. On the other hand, the improved YOLOv8n model maintains a high detection precision (mAP50: 95.6%) while further compressing the parameter count and computational complexity to 1.6M and 4.7G, respectively. Additionally, as depicted in Figure 15, the improved YOLOv8 network still exhibits significantly higher detection accuracy, lower parameter count, and network computational complexity compared to various lightweight convolutional networks. Finally, we conducted cross-comparisons of the foreign object detection model's recognition and detection results under different hardware conditions, as shown in Table 9. We conducted a total of three major comparative experiments. In the first experiment group, we evaluated several state-of-the-art detection networks mentioned in recent literature. Regardless of the hardware conditions, the network's parameter size remained unaffected, while recognition speed, i.e., frames per second, increased with GPU upgrades. In the second experiment group, we horizontally compared lightweight detection networks commonly used in the field of computer vision. From the results, it can be observed that the parameter size generally remained within 4 MB under the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Pallet** & **Anchor Shaft** & **Gangue** & **Angle Iron** & **Bolt** & **Nut** & **mAP50** & **Parameters** & **FLOPs** \\ \hline YOLOv3 & 90.3 & 91.4 & 91.6 & 92.0 & 91.6 & 80.1 & 91.6 & 61.6 & 193.5 \\ YOLOv5s & 91.3 & 92.8 & 92.1 & 91.3 & 93.7 & 80.6 & 91.4 & 7.2 & 16.5 \\ YOLOv5m & 91.6 & 92.7 & 94.5 & 91.3 & 93.5 & 83.6 & 92.9 & 21.2 & 49.0 \\ YOLOv5l & 94.9 & 96.9 & 97.8 & 97.1 & 97.1 & 84.9 & 94.5 & 46.5 & 109.1 \\ YOLOv5x & 95.4 & 97.3 & 97.8 & 97.6 & 97.2 & 88.1 & **96.9** & 86.7 & 205.7 \\ YOLOv7-tiny & 91.4 & 92.7 & 92.5 & 91.3 & 93.8 & 83.5 & 91.6 & 6.1 & 13.2 \\ YOLOv7 & 92.1 & 92.9 & 94.6 & 92.0 & 93.5 & 83.1 & 93.1 & 37.1 & 105.1 \\ YOLOv7-x & 94.9 & 95.2 & 95.1 & 94.9 & 93.6 & 83.5 & 94.1 & 70.5 & 188.6 \\ YOLOv8n & 92.9 & 93.7 & 96.5 & 95.1 & 94.5 & 71.4 & 92.3 & 3.1 & 8.1 \\ YOLOv8s & 95.4 & 96.9 & 98.0 & 97.5 & 97.3 & 84.6 & **95.9** & 11.1 & 28.6 \\ Improved YOLOv8n & 95.4 & 96.7 & 97.6 & 97.6 & 97.3 & 84.5 & 95.6 & **1.6** & **4.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of outcomes from various network detection models.