Algorithm for automatic identification of emergency service vehicles

The relevance of this research is determined by the need to ensure rapid access for emergency service vehicles to the territory of secured facilities, whose access in the modern urban environment is often restricted by automatically controlled barriers and other physical obstacles. This issue can be addressed by implementing intelligent identification systems for emergency service vehicles. Consequently, this paper aims to develop an algorithm for the automatic identification of emergency service vehicles based on images. The core idea of the proposed algorithm relies on the combined use of an artificial neural network and an ontological knowledge model of emergency service vehicles. The ontology was developed using the Protégé editor and the OWL language, based on an analysis of open data concerning the classification and equipment of emergency services. The YOLOv8 architecture, trained on an extended Roboflow dataset, was chosen as the foundation for the an artificial neural network. The results of the experimental study confirmed the high efficiency of the proposed model, achieving an accuracy of 89 %, which indicates its practical applicability for solving the target task. The developed algorithm can be integrated into intelligent access control systems for residential complexes and commercial facilities, thereby contributing to an increased level of safety and optimized service delivery.

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