Abstract. The research is devoted to solving the urgent problem of modernization of technological cycles at mining and processing plants. The main focus of the article is on automation of the primary stage of separation of fossil raw materials. Today, in many enterprises, quality control of rock mass is based on visual inspection and is carried out by the operator. It is generally believed that with this approach, the human factor introduces subjectivity into the assessment and reduces the accuracy of impurity fixation. That is why the article explores the possibility of implementing computer vision systems for operational sorting.
The research focuses on the development and testing of a binary classification method for digital images, which makes it possible to effectively separate streams into the target product (coal) and waste rock. In the framework of this work, the Random Forest algorithm was chosen as an architectural solution, the hyperparameters of which were optimized by the lattice search method. During the preliminary tests, the algorithm showed stable results in dusty conditions and changing lighting. To train and test the model, a data set of 4027 images of the mountain range was collected. The experiment was based on a comparative analysis of the proposed method with the methods of convolutional neural network (CNN), logistic regression and decision tree. The results confirmed the potential of this method. The model achieved a classification accuracy of 96.5% with an F1-score of 0.896 and a coal detection completeness of 85.7%. It has been found that with accuracy comparable to convolutional networks, the chosen algorithm has an advantage in resource efficiency and the ability to work on Edge devices without a GPU, providing performance of 30-35 FPS. The research results allow us to conclude that the achieved indicators, as well as the stability of the algorithm, make it possible to successfully integrate it into the monitoring system. The proposed solution can become the basis of an autonomous control system at a mining and processing plant without human intervention.

Keywords: computer vision, machine learning, Random Forest, rock classification, conveyor automation, coal industry.