AN APPROACH TO BUILDING ADAPTIVE OBJECT ACCOUNTING SYSTEMS USING ARTIFICIAL INTELLIGENCE METHODS

Abstract

The use of artificial intelligence methods for object accounting is associated with a number of difficulties, such as the variability of objects, the influence of shooting conditions, the overlap of objects in complex scenes, the need to work with different scales and high accuracy, as well as the presence of noise distortions in the data. The paper proposes an approach based on dynamic learning and adaptation to input data to organize the setup and operation of adaptive object accounting systems based on artificial intelligence methods, which includes several consecutive stages. The first stage is the semantic analysis of the user's request, which is based on the use of vector-graph data structure, which provides the allocation of semantically important elements of the request, allowing the system to understand the context of the task and adapt the strategy of search and classification of objects. Then follows the stage of automatic collection and preprocessing of data from open sources, which provides the expansion of the training sample and increases the stability of the model. The next important step is the generation of the training sample. This process includes image retrieval based on query semantics, manual validation and data partitioning, and initial training of the system for automatic partitioning. The above steps are repeated until the desired system performance is achieved. The iterative process of pre-training based on alternation of automatic markup and manual correction allows to reduce time expenditures on formation of training samples. The advantage of using vector-graph structure is the formation of more accurate semantic representation of information. Data augmentation including rotation, reflection, scaling, changing brightness and contrast, and adding noise is applied to enhance the generalization ability of the model. The proposed approach is designed to improve the efficiency (as the ratio of system operation time to its setup time) of object registration systems, ensuring their adaptability to different tasks and survey conditions