THE ISSUE OF TRAINING OF THE NEURAL NETWORK FOR DRONE DETECTION

In the article, the issue of mistaking birds for UAVs, which is considered one of the main problems in the process of detecting unmanned aerial vehicles (UAVs) through cameras, was considered. The object of the study is a method of training an artificial intelligence model so that the detection system does not mistake drones for birds. In order to solve this problem, as an alternative to creating a new complex model, the retraining method was used by making some changes to the previously developed model. The aim of the study is to train an artificial intelligence model using certain methods to detect drones and prevent them from being mistaken for birds. The main result of the research is the training of the artificial intelligence model with the application of the proposed methods for training the model and increasing the accuracy indicators. Also, a dataset was created for both classes, an algorithm was written to retrain the pre-trained model, a fine-tuning process was performed on the model to improve the training and validation performance, and the results were graphically expressed.