# Automatic Asbestos Control Using Deep Learning Based Computer Vision System +
###### Abstract
Moreover, in most cases, the image processing task is the object detection one [1; 2; 4]. Our previous work [6] referred to the development of the computer vision system for asbestos content estimation on the conveyor at the asbestos processing factory where the CNN was also successfully applied to semantic segmentation of the asbestos veins in the factory workshops [6]. The main advantages of the CNN-based approaches are the automation of the image feature extraction, selection, and transformation that are most relevant for the task. Figure 1: Typical photos of an open pit (**a**), rock, and asbestos veins there (**b**). The specific choice of features is determined by the training with the relevant examples. However, the well-training requires a large size of the input data instances [10]. The result of the current task analysis encouraged the research and design of the computer vision CNN-based system for asbestos productivity (content) estimation in an open pit. The discussed system needs to solve two tasks. The first is the fully automatic asbestos-containing rock chunks detection under open pit conditions. The second one is the asbestos productivity (content) estimation for each of the automatically detected rock chunks. Preliminary analysis of the discussed tasks supposes that the neural network framework should be considered firstly as an object detection or instance segmentation problem and the second one as a semantic segmentation problem. One of the most popular classes of the artificial neural network for semantic segmentation is based on the so-called U-Net architecture [11]. The U-Net is the CNN-based architecture that was initially proposed for the image analysis in medical applications where it shows high accuracy, in particular, for small objects [12; 13]. However, nowadays the U-Net-based approach is also popular in many of non-medical applications, in particular, the mining industry [1; 2; 3; 4; 5; 6; 7; 8]. For instance, in [1], the U-Net was applied for automatization of sorting gangue from the raw coal with the training only with 60 images (54 for training and 6 for the test). In [4] U-Net was applied for the automation of ore size distribution estimation by conveyor operation images. The same U-Net-based architectures can be applied even to the analyses of small size features with rare allocation on images, for instance, paper [14] apply the U-Net to segmentation of plant roots in the soil. One of the most popular classes of the artificial neural network for object detection is based on the Faster-R-CNN architecture [15] (including the Mask-R-CNN [16]). For instance, in [5] those architectures were applied to the rock chunks size estimation after blasting in an open pit. In addition, we recommend the following work in the tasks of the real-time scale object detection [17; 18; 19]. The analysis discussed above shows the feasibility of researching both the Mask-R-CNN-based architecture for instance segmentation and the U-Net-based one for semantic segmentation in the considered task. The aim of the paper is the discussion of the experience of research and design of the computer vision system for the automatic estimation of the asbestos productivity (content) in the chunks of rocks after blasting in the open pit of the LLC \"Ural Asbest\" (city Asbest, Sverdlovsk region, Russia) The mentioned open pit can be considered as one of the case studies. The obtained results are scaled to any other similar cases. It is necessary to note that this work is a continuation of the work described in [6]. However, the current task is more complex due to large distances from the camera to rock chunks, a high influence of the weather conditions (precipitation, natural light, seasons) and other factors. The novelty and contributions of this paper are the following. The paper proposed the first computer-vision-based approach to asbestos content estimation in a typical open pit and with the accuracy and time consumption values comparable with the work of geological specialists.