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The paper discusses the results of the first stage of research and development an innovative computer vision system for the automatic asbestos content control in stones veins at an asbestos processing factory. The discussed system is based on the applying of a semantic segmentation artificial neural networks, in particular U-Net based network architectures for solving both: the boundaries of stones segmentation and veins inside them. At the current stage, the following tasks were solved. 1. The discussed system prototype is developed. The system is allowing to takes images of the asbestos stones on the conveyor belt in the near-infrared range (NIR), avoiding the outer lighting influence, and processing the obtaining images. 2. The training, validation and test datasets were collected. 3. Substantiated the choice of the U-Net based neural network. 4. Proposed to estimate the resulted specific asbestos concentration as the average relation of all the veins square to all stones square on the image. 5. The resulted deviation between obtained and laboratory given results of the asbestos concentration is about 0.058 in the slope of graduation curve. The farther improvement recommendations for the developed system are given.

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