The paper considers and solves the issues of the formation of algorithms and practical methods for the application of machine vision in the concept of a smart oil storage. The resulting technology will significantly increase the autonomy of intelligent algorithms for managing the tank farm and ensure its safe operation. The fundamental principle of the development of the bionic approach in machine vision is the use of artificial neural networks to recognize objects from the received images. The solution to the problem of visual perception with the help of a computer was the development of artificial neural networks and algorithms for their training. To increase the autonomy of intelligent algorithms for managing a tank farm and ensure its safe operation, it is necessary to develop algorithms and practical methods for using machine vision in the framework of an intelligent oil storage unit. The results of measurements of a steel surface horizontal tank by a neural network based on the image from the camera are obtained by spatial transformation of its image and construction of structural elements using the contour segmentation algorithm. The initial representation, from which the mapping is carried out to some final representation, is usually the representation of the image in the form of an array of raw data - a set of results of physical measurements made for some image from the camera. A picture from a camera contains, in terms of computer vision, a set of physical objects or some fragment of the real world, in our case, a ground horizontal cylindrical tank. The smallest image element with such an initial representation is a pixel containing the result of a single measurement of a given physical quantity. The result obtained was: tank height 2.05 m, tank length 3.17 m, diameter 2.25 m. The measurement error in this case is 4.5%.