DEVELOPMENT OF A REAL-TIME UAV RECOGNITION MODEL BASED ON YOLOV10 NEURAL NETWORK

Abstract

The paper deals with the development of a model for real-time recognition and classification of UAVs and birds based on the training of the YOLOv10 neural network. The research area is considered relevant in connection with the problems of UAV detection in the context of security, given their growing use in various fields. A dataset consisting of 6255 images collected from proprietary archives and public resources is trained to train the model. The process of data annotation, augmentation and distribution was implemented using Roboflow.com service. The model was trained on NVIDIA GeForce RTX 4080 GPU using Ultralytics framework. Test results showed high recognition accuracy with mAP50 and mAP50-95 metrics exceeding previous versions of YOLO. The model demonstrates the ability for efficient object segmentation and tracking, which makes it promising for optoelectronic surveillance applications. The results of the study can be useful for developers of UAV and bird detection and classification systems, as well as for improving safety in various fields.