## 3 Results and Discussion
### Experimental Validation  
The amplitude of the vibrating distributor was adjusted. Specifically, the feeding amount was adjusted, and the image data in three different feeding states were obtained. Three groups of data sets were randomly selected. Each group of data sets contained 30 high-energy images, and each group of images corresponded to its feeding state. The number of coal and gangue in each data set was calculated through manual statistics, and the density distribution was represented by \(p=num/30\). The distribution densities of the three data sets were recorded as P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\), individually. Calculated: \(num_{1}=408\), \(num_{2}=754\), \(num_{3}=1643\), P\({}_{1}=13.6\), P\({}_{2}=25.13\), P\({}_{3}=54.76\).  
The statistical results of overlapping and adhesion targets of data sets are shown in Figure 15.  
When the feeding amount was small, the number of adhesion targets was small, and almost no adhesion occurred with more than four targets, as suggested in Figure 15. With the increase in feeding amount (distribution density \(p\)), the number of adhesion targets increased correspondingly, and the proportion of adhesion between two targets was the highest. Owing to no CLA in all statistical adhesion targets, the probability of CLA was extremely low. The algorithm in this paper was adopted to segment the adhesion targets with three different density distributions, and the segmentation results were counted (Figures 16 and 17).  
Figure 16: Accuracy of image segmentation.  
Figure 15: Distribution of adhesion targets.  
As shown in Figure 16, when the feed rate was low, the adhesion ratio of the two targets was high, and the segmentation accuracy was high. It shows that the algorithm had a good application effect on the overlapping and adhesion of two targets. Under P\({}_{3}\) density distribution, the segmentation quasi-curvature for two targets can reach 97.26%. With the increase in feeding amount, the probability of multi-target adhesion increased, and the segmentation accuracy decreased. Meanwhile, the overall segmentation accuracy can reach 90.73%. The OLCA and CLCA proposed in this paper can improve the segmentation accuracy when multiple targets are stuck together.  
As suggested in Figure 17a,b, the cases of wrong segmentation can be divided into over-segmentation and under-segmentation. There were three over-segmentations and two under-segmentations when the density distribution was P\({}_{2}\); there were 7 over-segmentations and 12 under-segmentations when the density distribution was P\({}_{3}\).  
According to the above experiments, two kinds of error segmentation are summarized.  
1. Irregular-shaped coal and gangue had concave defects, and more pits were detected, resulting in over-segmentation errors (Figure 18a);
2. Pit detection or minimum distance search method was adopted to obtain the wrong pit position and wrong dividing line (Figure 18b).  
Additionally, the wrong segmentation in Figure 18a generally occurred in a target area. This over-segmentation did not influence the subsequent recognition and location elimination. Therefore, it was still within the acceptable range. Regarding the adhesion targets with a density of P\({}_{3}\), there were only 12 severe false segmentations, with a total adhesion target of 205 and an acceptable segmentation accuracy of 94.15%.  
Figure 17: Segmentation results: (**a**) Over-segmentation result, (**b**) Under-segmentation result.