# Automatic Asbestos Control Using Deep Learning Based Computer Vision System +
## 2 Description of the Experimental System and Algorithm of Its Work
### Algorithm of the System Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Expert** & **Our Estimations** & **Effective Number** & **Number** \\  & **Estimations** & **After Threshold** & **of Chunks** & **of Images** \\ \hline
1 & 2.0\% & 1.66\% & 7 & 8 \\
2 & 2.0\% & 3.05\% & 10 & 10 \\
3 & 1.7\% & 3.7\% & 5 & 5 \\
4 & 4.0\% & 3.35\% & 11 & 12 \\
5 & 3.0\% & 2.41\% & 18 & 26 \\
6 & 1.5\% & 1.11\% & 8 & 8 \\
7 & 2.5\% & 1.9\% & 1 & 48 \\  & & 1.25\% & 1 & 49 \\
8 & 3.5\% & 1.48\% & 1 & 35 \\
9 & 2.5\% & 2.05\% & 1 & 45 \\  & & 2.0\% & 10 & 49 \\  & & 1.8\% & 3 & 45 \\
10 & 4.0\% & 1.82\% & 1 & 39 \\  & & 3.18\% & 1 & 58 \\  & & 2.0\% & 23 & 28 \\
11 & 2.5\% & 2.12\% & 39 & 39 \\  & & 2.31\% & 44 & 44 \\
12 & 1.5\% & 1.2\% & 18 & 63 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Asbestos productivity estimation for test data with threshold. Figure 13: Relation of our and experts estimated asbestos content values (in correspondence with Table 5); the ellipse shows normal and abnormal results inside and outside it correspondingly. The graduation (approximation) line in Figure 14 is obtained using the method of least squares. The line coefficients are: \(k=1.2,b=0.003\), with \(R^{2}=0.973\). The results are depicted on the graduation line with scatter up to 0.4%. ## 6 Conclusions

The investigation is carried out for the designed computer vision system for asbestos productivity (content) estimation under open pit conditions based on the deep learning neural network approach. During the work, the dataset of the open pits images and asbestos-containing rock chunks was collected. The dataset size is sufficient for training and testing the neural network both for the detection of rock chunks and asbestos veins inside them. The data was collected in the Ural Asbest open pit (Russia). The scheme for asbestos content (productivity) estimation was proposed and tested. The designed algorithm allows arranging fully automatical work (with auto-aiming). The Mask-RCNN-based architecture was applied to solve the instance segmentation problem for rock chunks detection in the open pit images. The U-Net-based network was applied to solve the semantic segmentation problem for the asbestos veins area and parameters estimation using the _Dice_ coefficient achieved 0.43 on the validation subset and 0.35 on the test data. The possibility of linear graduation between our estimations and geological service asbestos content estimations was shown. The error of about 0.4% after graduation was obtained for the test data (asbestos content about 1.5-4%). The results of the work have been discussed by the specialists of the LLC \"UralAsbest\", where the designed system was considered as a valuable tool for geological specialists instead of current visual-based asbestos content estimations. The system allows estimation of the current (prompt) overall asbestos content (productivity) for each separate open pit and without waiting for laboratory test results. The accuracy of its work is enough for the effective management of open pit and processing factories. The system measurement time is comparable with the one of specialist work. The presented work has some limitations, for instance, the dataset includes only images in the daytime. Such weather conditions as foggy or poor light in the early morning or late afternoon and other similar sorts have not been considered due to the lack of practical requirements, i.e., all the images were taken under similar conditions with the work of geological specialists. Moreover, this research considers only the Mask-R-CNN-based and U-Net-based architectures of the neural networks. Other types of architectures can be investigated further. However, the choice of these architectures can be explained by their popularity and high quality of the results in similar studies. In addition, we should mention the use of the specific camera only and in the distance range 5-10 m due to the requirement of vein resolution; in some other cases these results should be additionally investigated.