## 5 Conclusions  
In this paper, a new gangue sorting system with dual-energy X-ray recognition and improved YOLOv5 algorithm was proposed, which improves the YOLOv5-S network model by five methods: using EfficientNetV2 for the backbone network; designing the LPAN module in the neck and embedding the CBAM into the BottleneckCSP of the YOLOv5 feature fusion module; adding the L2 detection header in the head layer; choosing EIOU_Loss to replace the CIOU_Loss in the original network; and improving the recognition precision and decreasing the computational complexity. After conducting experiments to validate the methodologies, the following verdict was reached: in comparison with the YOLOv5-S, the updated model showed a 19.2% increase in mAP@5 and a 32.4% increase in mAP@5:95. Meanwhile, there was a 51.5% reduction in parameters, and a 14.7% reduction in computational complexity. In addition, the proposed method had better overall performance improvement as well as less resource consumption compared with other mainstream algorithms, which is more suitable for deployment and implementation on embedded devices. Specifically, the improved network architecture reduced 88.3%, 47.5%, and 86.4% in terms of model parameters, inference time, and computational cost, respectively, compared with YOLOv7 when the model size was only 7.10 MB. It is anticipated that the approach suggested in this study will be beneficial to a wider range of researchers in the identification and inspection of coal gangue on a conveyor belt, which holds significant practical application value for the intelligent sorting of coal gangue. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Params (M)** & **FLOPs (G)** & **mAP@5 (\%)** & **[EMAIL_ADDRESS] (\%)** & **Latency/MS** & **Model Size (MB)** \\ \hline NanoDet-Plus & **1.17** & **3.72** & 62.18 & 25.38 & **21.30** & **2.24** \\ YOLOv5-N & 1.85 & 4.55 & 66.51 & 29.93 & 22.80 & 3.83 \\ YOLOv5-S & 7.26 & 16.92 & 73.40 & 37.90 & 27.40 & 14.20 \\ YOLOv5-M & 21.56 & 51.82 & 84.82 & 46.79 & 31.60 & 43.20 \\ Improved & & & & & & \\ YOLOv5-S & 3.52 & 14.43 & **87.50** & **50.20** & 25.30 & 7.10 \\ YOLOv7 & 37.62 & 106.47 & 84.53 & 46.95 & 48.20 & 76.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing the property of dissimilar excellent algorithms on self-constructed gangue dataset. In our future endeavors, we will focus on optimizing test results for hardware devices with low computational power, i.e., high quality inference tests with limited computational power. Additionally, we will conduct in-depth research on image preprocessing techniques to minimize and avoid the impact of light artifacts. Conceptualization, Y.Q. ; Funding acquisition, Z.K. ; Investigation, Y.W. ; Methodology, Y.Q. ; Project administration, Z.K. ; Software, Y.Q. ; Validation, Y.Q. ; Writing--original draft, Y.Q. ; Writing--review & editing, Z.K. and C.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (52174147) the Research on Underground Complex Working Condition Inspection Robot Based on Domestic Dragon Core Control (a Key R&D Plan Project in Shanxi Province), the Shanxi Province Science and Technology Innovation Leading Talent Team Project on Mining Equipment Artificial Intelligence and Robotization Technology (project number:202204051002003), the Key Technology Research and Industrialization of Intelligent Lower Transportation Expandable Belt Conveyor Equipment (Taishan Industry Leading Talent Program), and the Unattended Management System of Belt Conveyor Based on Multi-sensing Technology. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. The authors express their gratitude to [PERSON] and [PERSON] from Shanghai Shamin Intelligent Technology Co., Ltd. for their invaluable contribution in providing generalized data.