# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 4 Experiments
### General Detection Performance Evaluation  
We evaluate the detection performance of the proposed CAFOD model under general settings, where the testing images originate from the same cameras used in the training phase. The results, summarized in Table 3, are reported in AP50 for each object category (Big coal, Wood, Iron bars, Iron mesh) and mAP50 for overall detection performance. The results demonstrate that CAFOD achieves the highest mAP50 score of 80.39, outperforming all baseline models. This improvement highlights the effectiveness of the proposed modules in enhancing Deformable DETR, enabling it to achieve State-of-the-Art performance in the coal mine conveyor belt foreign object detection task.  
Comparing the overall performance of the baselines, we can make the following key observations: (1) Models focusing on learnable and deformable proposals, such as Sparse-RCNN (73.10) and Deformable DETR (74.76), fail to achieve high accuracy in the complex coal mine conveyor belt environment. While these models effectively reduce the number of candidate boxes, they struggle with background clutter, leading to a drop in detection performance. This highlights the challenge posed by complex, non-uniform backgrounds in foreign object detection tasks. (2) Methods optimizing label assignment strategies, such as AutoAssign (75.45) and O2F (74.82), achieve better performance than Sparse-RCNN. This suggests that improving classification label assignment contributes to performance gains. Similarly, VarifocalNet (75.26) enhances classification confidence through its IoU-aware loss function, further validating that optimizing the classification head can yield improvements. (3) As foreign objects must be located on the conveyor belt, integrating contextual features from the surrounding coal can enhance detection accuracy. Models like CentripetalNet (76.61) and AFPN (77.42) improve representation learning by utilizing high-quality keypoints and hierarchical feature fusion, respectively. These enhancements allow them to integrate surrounding object features, leading to performance  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Big** & **Wood** & **Iron Bars** & **Iron Mesh** & **All** \\ \hline Autoassign (2020) [18] & 56.32 & 87.81 & 84.15 & 73.52 & 75.45 \\ Sparse-RCNN (2021) [22] & 49.81 & 82.97 & 81.80 & 77.83 & 73.10 \\ Deformable DETR (2021) [27] & 57.11 & 83.92 & 81.17 & 76.85 & 74.76 \\ VarifocalNet (2021) [10] & 58.72 & 85.10 & 83.33 & 73.92 & 75.26 \\ CentripetalNet (2021) [21] & 60.53 & 87.91 & 83.92 & 74.15 & 76.61 \\ O2F (2023) [17] & 55.38 & 88.72 & 81.50 & 73.71 & 74.82 \\ CEASC (2023) [19] & 55.53 & 87.15 & 84.23 & 70.12 & 74.25 \\ AFPN (2023) [20] & 60.61 & 83.64 & 83.91 & 81.55 & 77.42 \\ CAFOD (ours) & 65.41 & 88.95 & 83.86 & 83.37 & 80.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall Performance Comparisons (measured by AP50/mAP50).  
gains. However, CEASC (74.25), which incorporates a global context module, does not achieve as much improvement. This may be due to excessive inclusion of features outside the conveyor belt region, diluting the focus on relevant foreign object regions.