基于卷积神经网络的无人机识别方法仿真研究

为了提高卷积神经网络(CNN)的泛化性和鲁棒性,改善无人机航行时识别目标图像的精度,提出了一种CNN与概率神经网络(PNN)相结合的混合模型。利用CNN提取多层图像表示,使用PNN提取特征对图像进行分类以替代CNN内部的BP神经网络,采用均方差和降梯度法训练模型,通过将预处理的图像传输到CNN-PNN模型,对图像纹理和轮廓进行分类识别,并将此模型的仿真结果与卷积神经网络模型、卷积神经网络-支持向量机模型的结果进行对比。仿真结果表明,与其他两种模型相比,CNN-PNN模型具有更好的精准度,识别率高达96.30%。因此,CNN-PNN模型能够快速有效地识别图像,准确度和实时性较高,在图像识别等方面具有很好的应用前景。

Simulation research on UAV recognition method based on convolutional neural network

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.