# Camera-Adaptive Foreign Object Detection for Coal Conveyor Belts
## 5 Conclusions  
In this work, we propose CAFOD, a camera-adaptive foreign object detection model tailored for coal conveyor belts in coal mines. Motivated by the challenge of viewpoint variations and background interference in real-world environments, we introduced four key modules Multi-View Data Augmentation (MVDA), Context Feature Perception (CFP), Conveyor Belt Area Loss (CBAL), and Varifocal Loss (VFL)--to enhance the model's \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Stage** & **Methods** & **Parameters** & **GPU** & **Memory** & **Time** \\ \hline \multirow{8}{*}{Training Phase} & Autoassign & 285.78 MB & 8346 MB & 5214 MB & 50,435 s \\  & Sparse-RCNN & 1219.75 MB & 9566 MB & 6991 MB & 64,082 s \\  & Deformable DETR & 471.52 MB & 17,998 MB & 13,790 MB & 57,600 s \\  & VarifocalNet & 249.19 MB & 8860 MB & 5720 MB & 89,287 s \\  & CentripetalNet & 2365.44 MB & 11,546 MB & 10,665 MB & 79,235 s \\  & OZF & 246.31 MB & 5742 MB & 3374 MB & 61,261 s \\  & CEASC & 324.92 MB & 8531 MB & 2521 MB & 69,488 s \\  & AFPN & 31.4 MB & 6531 MB & 2109 MB & 58,804 s \\ \cline{2-6}  & CAFOD (ours) & 479.48 MB & 22,392 MB & 19,255 MB & 61,200 s \\ \hline \multirow{8}{*}{Inference Phase} & Autoassign & 285.78 MB & 6966 MB & 735 MB & 0.1450 s \\  & Sparse-RCNN & 1219.75 MB & 9566 MB & 1947 MB & 0.1759 s \\  & Deformable DETR & 471.52 MB & 13,868 MB & 1127 MB & 0.2186 s \\  & VarifocalNet & 249.19 MB & 15,272 MB & 806 MB & 0.6561 s \\  & CentripetalNet & 2365.44 MB & 8748 MB & 5342 MB & 0.7804 s \\  & OZF & 246.31 MB & 5742 MB & 635 MB & 0.2015 s \\  & CEASC & 324.92 MB & 8531 MB & 985 MB & 0.2041 s \\  & AFPN & 31.4 MB & 6531 MB & 531 MB & 0.0510 s \\ \cline{2-6}  & CAFOD (ours) & 479.48 MB & 1242 MB & 965 MB & 0.2078 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model Complexity Comparision. robustness and generalization across multiple camera perspectives. The introduction of MVDA enables the model to learn viewpoint-invariant features, improving its adaptability to varying camera perspectives. CFP incorporates surrounding coal features to reduce false detections of irrelevant background objects outside the conveyor belt. CBAL enforces the model's attention on the conveyor belt region, ensuring that detection focuses on targets within the belt area, while VFL enhances the model's sensitivity to hard samples, improving overall detection accuracy. Extensive experiments on a self-collected dataset from three cameras in real coal mine environments demonstrate that CAFOD significantly outperforms the baseline Deformable DETR and other contemporary methods, especially under cross-camera evaluation settings. Compared with conventional object detection models that do not explicitly address multi-view variability and background interference, CAFOD achieves higher detection precision and superior generalization across different viewpoints. These results highlight the effectiveness and uniqueness of CAFOD in handling practical coal mine detection scenarios. The proposed method offers a promising solution for enhancing production safety and operational efficiency and is particularly suitable for deployment on edge devices in industrial applications. Conceptualization, F.P. and X.L. ; methodology, F.P. ; software, F.P. and K.H. ; resources, F.P. and X.L. ; writing--original draft preparation, F.P., K.H. and X.L. ; writing--review and editing, K.H. and X.L. ; funding acquisition, F.P. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (No. 62276162). Not applicable. Not applicable  
The data presented in this study are available on request from the author. The authors are extremely grateful to other researchers for their valuable contributions to this manuscript. [PERSON] provided industrial case studies and practical data, enriching the research with real-world insights. [PERSON] and [PERSON]'s meticulous review and editing significantly improved the clarity and coherence of the manuscript.