## 3 Results and Discussion
### Experimental Comparison  
This paper verifies the algorithm's performance by setting contrast experiments. Various methods processed a group of image data, and the final segmentation results were compared. Method 1: a watershed algorithm based on distance transformation, method 2: Method proposed by reference [11], method 3: Method proposed by reference [32], and method 4: The algorithm in this paper. The processed data include 274 adhesion targets, among which 127 are adhered by two targets, 103 by three targets, 31 by four targets, 13 by more than four targets, and 4 by a closed loop. The specific segmentation results are shown in Table 1.  
As seen in Table 1, the algorithm proposed in this paper can segment 274 adhesion target areas with an accuracy of 91.24%. Its advantage is that it can better find all pits, thus completing the segmentation of multi-target adhesion. Conjugate lines to detect pits can improve the overall segmentation accuracy by 6.57% compared with a single line. Significantly, the segmentation accuracy of the three targets can be improved by 5.83%. Still, the segmentation accuracy of double target adhesion was not greatly improved. Compared with the WA and the method proposed in reference [11], this method can solve the segmentation problem of multi-objective, multi-scale, and multi-type irregular coal and gangu  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Comparison of Segmentation Methods** & **Adhesion Target** & **2-Adhesion** & **3-Adhesion** & **4-Adhesion** & **\textgreater{}4-Adhesion** & **CLA** \\ \hline \multirow{4}{*}{Method 1} & Total & 274 & 127 & 103 & 31 & 13 & 4 \\  & Correct & 203 & 102 & 78 & 18 & 5 & 3 \\  & Over-segmentation & 40 & 14 & 16 & 5 & 5 & 1 \\  & Under-segmentation & 31 & 11 & 9 & 8 & 3 & 0 \\  & Accuracy \% & 74.08 & 80.31 & 75.72 & 58.01 & 38.46 & 75 \\ \hline \multirow{4}{*}{Method 2} & Correct & 177 & 84 & 72 & 17 & 4 & 1 \\  & Over-segmentation & 51 & 32 & 11 & 5 & 3 & 0 \\  & Under-segmentation & 46 & 11 & 20 & 9 & 6 & 3 \\  & Accuracy \% & 64.59 & 66.14 & 69.90 & 54.83 & 30.76 & 25 \\ \hline \multirow{4}{*}{Method 3} & Correct & 232 & 122 & 87 & 18 & 5 & 2 \\  & Over-segmentation & 7 & 5 & 1 & 1 & 0 & 0 \\  & Under-segmentation & 35 & 0 & 15 & 12 & 8 & 2 \\  & Accuracy \% & 84.67 & 96.06 & 84.46 & 58.01 & 38.46 & 50 \\ \hline \multirow{4}{*}{Method 4} & Correct & 250 & 123 & 93 & 24 & 10 & 3 \\  & Over-segmentation & 6 & 4 & 1 & 1 & 0 & 1 \\ \cline{1-1}  & Under-segmentation & 18 & 0 & 9 & 6 & 3 & 0 \\ \cline{1-1}  & Accuracy \% & 91.24 & 96.85 & 90.29 & _77.42_ & 76.92 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Segmentation results in overlapping and adhesion targets.  
Figure 18: Error segmentation target: (**a**) Too many pits are detected, resulting in over-segmentation, (**b**) A pit detection error or a minimum distance finding error results in a segmentation error.  
Some of the ubiquitous under-segmented objects can be segmented again after the second recognition is confirmed as a stuck object, as shown in the feedback loop process in Figure 12. Finding the dividing line on the basis of the minimum distance search method can compensate for the segmentation problem of missing pits in partial pit detection. Still, when multiple targets are stuck together, it is easy to be affected by concave defects. The wrong dividing line was found, which led to the incorrect segmentation or under-segmentation of the image. The principle of filtering unwanted concave defects and matching pits will still be an essential issue to improve the segmentation accuracy further.