## 4 Limitations and Future Work
### Accuracy Degradation Due to Scanning Distance and Dust Air  
In this study, a lab-scale model experiment simulating an actual mine site was performed. In the configuration of the experimental site, the distance between the rock and the camera was closely arranged. However, in an actual mining site, a camera must be placed at a high position to scan a large-scale hopper. As the height of the camera increases, the size of each mineral will appear smaller, which will reduce the object recognition accuracy. Of course, in an actual mining site, as the distance of the camera increases, the size of the mineral increases, so recognition is not completely impossible. However, a high-resolution RGB-D camera that reflects the scale of the site and the area of the hopper should be utilized, and a machine vision system should be developed in detail.  
At the actual mining site, a large amount of dust will be generated by mineral crushing. This will limit the camera's field of view and reduce the object recognition accuracy. In this study, the chroma and transparency of RGB images were partially adjusted to confirm whether objects were recognized when the dust air environment was simulated. Figure 16 shows the results of the object recognition accuracy experiment for RGB images simulating dust air by adjusting chroma and transparency. Overall, it was confirmed that the rocks at the top and bottom of the hopper were recognized separately from the background. However, when there was a rock under the hopper, it was confirmed that the shape was partially unclear.  
The rock recognition results in an environment without dust air are compared with the recognition results in an environment simulating dust air. As a result of rock recognition in Figure 16 and Table 4, all rocks were recognized in the same way as in an environment without dust air, and since the center coordinates of rocks were also calculated almost the same, the accuracy was evaluated through the area of the part recognized as a rock. When there are three rocks on the hopper (Figure 16b), it can be confirmed that the area of the part recognized as a rock is calculated to be narrower by about 2% compared to the non-dust environment. When there are three rocks on the hopper and one rock below (Figure 16d), it was confirmed that the rocks on the hopper were recognized as narrow by about 2% similar to the previous case. On the other hand, the rock under the hopper was recognized to be about 5% smaller depending on the presence or absence of dust air, showing a relatively large difference. Since the intensity of the light transmitted to the rock under the hopper is relatively weak, it was confirmed that it was relatively greatly affected by the light limitation by dust air.  
In an actual mining environment, the concentration of dust air is not constant and may be so large that the shape of the mineral is not visible at all. In addition, it can work together with the lighting intensity and shadows of the scene to further reduce the object recognition accuracy. Therefore, to overcome this limitation in actual mining sites, an additional device to periodically measure the dust air concentration using environmental sensors and then remove it will be needed. In addition, a sophisticated image processing algorithm that reflects various conditions in the field should be applied.