## 4 Experimental Results and Analysis
### Analysis of Generalization Performance Experiment Results  
#### 4.4.1 Analysis of Experimental Results of Different Data Sets  
Alongside the mAP and model parameter count, the neural network model's capacity for generalization serves as one of the metrics for assessing model quality. We expect that the model, trained on the dataset, will deliver a sensible output when presented with new samples not included in the dataset. Because of the particularity of the application context, there is currently no openly accessible dataset for detecting foreign objects on belt conveyor systems. Manually introducing foreign objects into coal mine belt conveyor systems would contravene safety regulations. Considering safety concerns, a validation was conducted at Boshitong Limited in Taiyuan, Shanxi Province, China (dataset containing 500 foreign object images, referred to as DataI). The assessment outcomes are displayed in Table 6, while certain detection findings are depicted in Figure 11.  
Figure 11: Data I recognition result diagram. (**a**) Original image; (**b**) YOLOv8n recognition result; (**c**) improved YOLOv8n recognition result. It can be observed that both the original and improved network models achieve relatively accurate detection results for large pieces of coal gangue and other foreign objects. However, the improved model accurately detects buried and small corner foreign objects while avoiding redundant detection boxes. To further assess the model's capacity for generalization, we applied various image techniques to adjust some images in the Datal dataset, simulating real underground mining conditions such as fog, dust, low lighting, and blurriness due to robot motion during the inspection process. The detection outcomes are depicted in Figure 12 (the leftmost column signifies mild processing, the central column signifies moderate processing, and the rightmost column signifies severe processing). The detection results show that the object detection network benefits from data augmentation techniques during model training, enabling it to exhibit strong adaptability to environmental changes. Specifically, the detection results remain largely unaffected in the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data I** & **mAP (\%)** & **Parameters (M)** & **FLOPs (G)** \\ \hline YOLOv8n & 90.9 & 3.2 & 8.2 \\ Improved YOLOv8n & **93.6** & **1.6** & **4.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of Data I before and after improvement. Figure 12: 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. presence of dust and fog. Even under low lighting conditions, the model can accurately identify buried objects. Although we considered motion blur and applied the corresponding preprocessing to the data before training, severe image blurriness can still lead to some missed detections. Furthermore, this research assessed the new model's generalization capability using data from Reference [18] (dataset containing 10,448 images with six types of non-coal foreign objects, denoted as DataII). The assessment outcomes are showcased in Table 7, and partial recognition findings are depicted in Figure 13. The visualized results show that both the original and improved networks exhibit commendable recognition and detection capabilities for large foreign objects. However, when dealing with partially buried objects and objects with features similar to the background, the improved network demonstrates higher detection accuracy, as indicated by the higher confidence scores of the detection bounding boxes. Combining the findings from Table 7, it is evident that the improved network has a lower parameter size and reduced model complexity. Likewise, we employed identical data augmentation methods for the DataII dataset as we did for DataI, and the detection results are shown in Figure 14 (the leftmost column