The article examines the possibility of using convolutional neural networks for technical object recognition in the context of radio monitoring. The focus is on the development and optimization of algorithms for processing radar signals using deep neural networks. Studies have shown that the use of CNN can significantly improve the classification accuracy of radio signals compared to traditional processingmethods. The developed approach is based on the extraction of hierarchical features from spectral images of radio signals and their subsequent classification using a trained neural network. The paper presents the results of experimental studies conducted on a dataset of more than 10,000 samples of radio signals of various types. It is shown that the proposed technique ensures recognition accuracy of up to 94% when working with noisy signals and the probability of a false alarm is no more than 0.05. Special attention is paid to the choice of neural network architecture for the specifics of the radio monitoring task. The options for converting radio signals into a spectral image for real-time processing were also considered in detail. Data preprocessing methods have been developed, including amplitude normalization, frequency correction, and interference elimination. The results of the study can be used in radio broadcast control systems and to ensure electromagnetic compatibility of electronic devices. The results obtained demonstrate the prospects of using CNN in the tasks of technical recognition of radio monitoring objects and open new opportunities for the development of intelligent radar information processing methods. Promising areas of further research include the development of adaptive neural network training methods in a changing radio environment and the creation of hybrid systems combining traditional signal processing methods with modern neural network algorithms
