## 5 Conclusions  
In this study, a smart hopper system was proposed based on a machine vision-based image-processing technique, an RGB-D camera, and a collaborative robot. The proposed system captures RGB and depth images using an RGB-D camera and processes it using a machine vision-based image recognition technique in a real-world hopper to detect rocks inside the hopper and calculate their coordinates. In addition, it simulates the movement of the robot arm based on the detected coordinates, enabling it to be maneuvered to remove blockages in the real world. The recognition accuracy of the smart hopper system was  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Rock 1**} & \multicolumn{2}{c}{**Rock 2**} & \multicolumn{2}{c}{**Rock 3**} & \multicolumn{2}{c}{**Rock 4**} \\ \hline  & **Non-Dust** & **Dust** & **Non-Dust** & **Dust** & **Non-Dust** & **Dust** & **Non-Dust** & **Dust** \\ \hline Area (pixel) (Figure 16b) & 371 & 364 & 268 & 262 & 287 & 280 & & \\ \hline Area (pixel) (Figure 16d) & 202 & 197 & 222 & 216 & 226 & 220 & 244 & 232 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of the detected rock area according to the presence or absence of simulated dust air.  
Figure 16: Experimental result of the smart hopper when the dust air environment is simulated by adjusting the image chroma and transparency. (**a**) RGB image with 3 rocks on the hopper, (**b**) binary image of detected 3 rocks, (**c**) RGB image with 3 rocks on the hopper and one rock under the hopper, (**d**) Binary image of detected 4 rocks.  
evaluated with respect to the location and quantity of rocks. The results confirmed that the rocks were accurately recognized in all situations.  
Equipment such as crushers and hoppers used in existing mining sites may be obstructed when a large number of rocks or large rocks are dispensed into the hopper. These rocks are usually removed manually, which poses a safety risk and reduces productivity by delaying the workflow. The utilization of the smart hopper system and robot arm developed in this study is expected to provide an efficient solution to these problems. In addition, a simulation system has been implemented to recognize and process images in the virtual world. The robot arm was directly controlled based on the results and the proposed system was optimized. This paper is also expected to serve as a useful reference for the implementation of the vision system and robot technology in the mining sector in the future.  
Conceptualization, Y.C.; Data curation, Y.C.; Funding acquisition, Y.C.; Investigation, H.K. and Y.C.; Methodology, H.K. and Y.C.; Project administration, Y.C.; Resources, Y.C.; Software, Y.C.; Supervision, Y.C.; Validation, H.K.; Visualization, H.K.; Writing--original draft, H.K.; Writing--review & editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1A09083947) and by the Ministry of Science and ICT (MSIT) (2021R1A2C1011216).  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.