# Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
## 4 Experiment
### Results and Discussion  
Similar to other studies, classification accuracy is an overall measure of the LTC-Net method and other baselines. However, the recall rate of coal and the recall rate of gangue should also be a key indicator of effectiveness and robustness. On the one hand, the numbers of coals and gangues are balanced in our testing sets. Therefore, the offline method could, by chance, have an accuracy greater than 50%. On the other hand, the ratio of coals to gangues can be very unbalanced in real industrial workshops, and hence the methods should be evaluated based on all of the above indexes. A high recall rate of coal in mines with a low percentage of gangue leads to the failure of the separation system. On the contrary, coal mines with a high percentage of gangue result in a very high waste of valuable coal. As shown in Tables 3-6, the LTC-Net method is testified stable and accurate on the four testing datasets. On Dataset1_test, the homogenous dataset of the training set Dataset1_train, LTC-Net secure 97.8% accuracy while baseline methods also have fairly good performances. However, when tested on non-homogenous datasets, baseline methods show a decreasing accuracy while the LTC-Net method can still have decent successful classification rates for both coals and gangues, even on Dataset4, the resolution of which is much lower. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **Dataset1\_test** & **Dataset2** & **Dataset3** & **Dataset4** \\ \hline Gray Value + SVM & 0.700 & 0.740 & 0.577 & 1.000 \\ \hline GLCM + SVM & 0.583 & 0.060 & 0.538 & 0.933 \\ \hline CNN (VGG16) & 1.000 & 0.833 & 0.706 & 1.000 \\ \hline LTC-Net & 1.000 & 0.939 & 0.846 & 0.802 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average accuracy of four datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **Dataset1\_test** & **Dataset2** & **Dataset3** & **Dataset4** \\ \hline Gray Value + SVM & 0.880 & 0.680 & 1.000 & 0.000 \\ \hline GLCM + SVM & 0.684 & 0.980 & 1.000 & 0.833 \\ \hline CNN (VGG16) & 0.801 & 0.960 & 0.597 & 0.111 \\ \hline LTC-Net & 0.960 & 0.979 & 0.836 & 0.778 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recall of coal. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **Dataset1\_test** & **Dataset2** & **Dataset3** & **Dataset4** \\ \hline Gray Value + SVM & 0.700 & 0.740 & 0.577 & 1.000 \\ \hline GLCM + SVM & 0.583 & 0.060 & 0.538 & 0.933 \\ \hline LTC-Net & 1.000 & 0.833 & 0.706 & 1.000 \\ \hline LTC-Net & 1.000 & 0.939 & 0.846 & 0.802 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recall of gauge. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Dataset**} \\ \cline{2-5}  & **Dataset1\_test** & **Dataset2** & **Dataset3** & **Dataset4** \\ \hline Gray Value + SVM & 0.800 & 0.710 & 0.772 & 0.485 \\ \hline GLCM + SVM & 0.645 & 0.520 & 0.752 & 0.879 \\ \hline CNN (VGG16) & 0.914 & 0.898 & 0.655 & 0.521 \\ \hline LTC-Net & 0.978 & 0.959 & 0.841 & 0.788 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy. method. By dividing the full-scale images into local texture ones, the affection of the inherent similarity problem is mitigated since the classification result of the LTC-Net method is not given directly from a full-scale image or some parameters extracted from a full-scale image. The benefit of this method is concluded. * The local texture images are more separable and have marked distinctions (larger inter-class distance), which leads to a higher recognition accuracy than those trained on full images. After all, the non-homogenous images could be considerably different in full-scale but more similar at a local texture scale.