## 3 Results  
Figure 12 illustrates the experimental results obtained using the proposed method in a case involving one rock under the hopper and another above the hopper. The rock on the hopper was located without any protrusions over the edge of the hopper (Figure 12a). Both rocks were accurately recognized using the RGB image-processing technique, as is evident from Figure 12b). The rock located under the hopper was already being crushed in the gyratory crusher or was about to be crushed; therefore, it had to be excluded from the movement path planning phase for the robot arm. By superimposing the depth images of the two rocks and the hopper, only the rock placed on the red background in Figure 12c) was identified to be the final detection object. It was confirmed that only that rock was removed (Figure 12d). Figure 13 depicts another example involving one rock above the hopper and two below it, where the latter are partially hidden by the column of the crusher or already in the process of being crushed, thus making their shape appear irregular. In the image, the rock above the hopper is placed across the opening between the hopper and crusher column, which visually obstructs the space below the hopper. As the depth image was updated in real-time whenever rocks were dispensed into the hopper, the background of the part blocked by the rocks was displayed in red as in the case of the hopper and its upper surface. This indicates that the rock lies on the same level as the hopper. In this  
Figure 11: Conceptual diagram and photograph of the miniature smart hopper system inside the laboratory. case of the experiment, the robot arm successfully removed only the rock lying across the opening of the hopper and the crusher column and ignored the two rocks under the hopper. This confirms that the machine vision-based image-processing algorithm developed in this study successfully detects rocks above the hopper in various positional situations. Figure 12: Experimental results obtained using the proposed smart hopper system in the case involving one rock under the hopper and one above the hopper (on the edge of the hopper). (**a**) RGB image, (**b**) detected rock in the binary image (**c**) classified rock based on depth image, and (**d**) operation of the robot arm. Figure 13: Experimental results obtained using the proposed smart hopper system in a case involving two rocks under the hopper and one across of the hopper. (**a**) RGB image, (**b**) binary image of detected rock, (**c**) classified rock based on depth image, and (**d**) operation of the robot arm. Figure 14 depicts a case involving two rocks placed on the hopper. The upper one is located partially over the bottom of the hopper, while the other is clearly above the hopper. Both rocks were detected using the proposed RGB image processing technique. In addition, it was confirmed that both rocks were removed by the robot arm. Figure 15 depicts a case with three rocks placed together on a hopper--one on top of the hopper, one at the meeting point of the crusher column and the hopper, and one partially intersecting the crusher and the hopper. It can be seen that all three rocks were detected using RGB image processing. By superimposing the images of the individual rocks with the depth image, it was confirmed that all of the rocks were placed on the hopper. Although irregular noise was generated at some points within the hopper in the depth image, it did not affect the recognition of the positions of the rocks (Figure 15c). Based on the RGB image, all three rocks were sequentially removed from the hopper (Figure 15d-f). Table 3 lists the RGB-D coordinates of the detected objects depicted in Figures 12-15 and the transformed coordinates corresponding to the robot arm. One to three objects were detected in all cases, including those in which rocks were placed across hoppers and crusher columns. It was confirmed that the objects under the hopper were detected by the camera and finally excluded based on the depth image processing technique.