Simulation research on UAV recognition method based on convolutional neural network

Abstract

In order to improve the accuracy of identifying the target image while the drone is sailing, a hybrid model combining convolutional neural network (CNN) and probabilistic neural network (PNN) is proposed. The model uses CNN to extract multi-layer image representations and uses PNN extraction features to classify images. In order to improve the generalization and robustness of CNN, CNN-PNN model replaces BP neural network inside CNN with PNN, and trains the model by mean square error and gradient reduction method. The pre-processed image is transmitted to the CNN-PNN model to classify and identify the texture and contour of the image, and the simulation results of this model are compared with the results of convolutional neural network model and convolutional neural network-support vector machine model. The simulation results show that the CNN-PNN model has better accuracy compared with the two models, and the recognition rate is as high as 96.30%. The improved model improves the generalization and robustness of CNN, and can enhance the accuracy of image recognition in all aspects, and has high real-time performance.

Keywords

• image identification
• UAV recognition
• falling gradient method
• probabilistic neural network
• convolutional neural network