# Automatic Asbestos Control Using Deep Learning Based Computer Vision System +
###### Abstract  
The paper discusses the results of the research and development of an innovative deep learning-based computer vision system for the fully automatic asbestos content (productivity) estimation in rock chunk (stone) veins in an open pit and within the time comparable with the work of specialists (about 10 min per one open pit processing place). The discussed system is based on the applying of instance and semantic segmentation of artificial neural networks. The Mask R-CNN-based network architecture is applied to the asbestos-containing rock chunks searching images of an open pit. The U-Net-based network architecture is applied to the segmentation of asbestos veins in the images of selected rock chunks. The designed system allows an automatic search and takes images of the asbestos rocks in an open pit in the near-infrared range (NIR) and processes the obtained images. The result of the system work is the average asbestos content (productivity) estimation for each controlled open pit. It is validated to estimate asbestos content as the graduated average ratio of the vein area value to the selected rock chunk area value, both determined by the trained neural network. For both neural network training tasks the training, validation, and test datasets are collected. The designed system demonstrates an error of about 0.4% under different weather conditions in an open pit when the asbestos content is about 1.5-4%. The obtained accuracy is sufficient to use the system as a geological service tool instead of currently applied visual-based estimations. deep learning; computer vision systems; asbestos content control; object detection; semantic segmentation +
Footnote †: _Applied_  
operational information about the current productivity of asbestos in an open pit. At the time the laboratory analysis provides the precision evaluation of productivity, but is expensive and requires almost one day, thus it can be only considered as an average estimation of the asbestos output within a time shift (about 8 h). The results of the evaluation made by the geological service (visual analysis) could be very subjective. In most cases, these results differ from the laboratory ones. Moreover, the estimations made by different experts may vary significantly [7]. As a rule, geological service specialists cannot describe formally their algorithms or criterion how they make estimations of asbestos content in an open pit. Such estimation techniques are difficult to describe scientifically. Moreover, it requires much time and is rather expensive to train these specialists. The reasons mentioned above lead to the conclusion that there is the rationale of automatic system design for the operative productivity evaluation of valuable rocks resources mining, particularly, the asbestos mining in the open pit. It is necessary to clarify the meaning of the productivity of asbestos mining that is the relative content of asbestos fiber in the veins of rock chunks obtained after blasting of some wall in an open pit. The typical photos of an open pit, rock, and asbestos veins there are shown in Figure 1,b. The discussed rock chunks are collected and moved to the processing factory where the asbestos fibre is produced. For the effective management of an open pit and factory, it is necessary to evaluate and control the current productivity of asbestos fibre. The analysis of the known engineering solutions in the mining industry has shown that contemporary computer vision systems allow the automation of such mining processes as coal and rocks classification on the conveyor [2]; the rocks and coal sorting by size [1] and types [3]; the ore size determination [4]; the fossil identification and classification in ore [8]; the asbestos fiber size estimation under microscopy [9]; and other tasks. In most denoted and other cases, the computer vision systems for automating the mining industry are based on image processing using artificial neural networks, namely convolution neural networks (CNN) [1; 2; 3; 4; 8; 9].