Target Recognition For Unmanned Vehicle System Based On Deep Learning

With the rapid development of artificial intelligence technology,unmanned vehicle has become one of the hotspots of current social research.Target recognition is one of the important steps for unmanned vehicle.Through the processing and analysis of the data collected by the sensor,the vehicle can perceive and judge the surrounding environment.With the successful application of deep learning in the field of computer vision and the improvement of computer hardware level,the target recognition based on deep learning methods has received a lot of attention and research from researchers.Based on above two points,this paper studies the target recognition for unmanned vehicle system based on the deep learning algorithm.The main contents of this paper are as follows.Firstly,a detailed study of deep learning basic knowledge related to the content of this article is studied.The functions of the convolution kernels,pooling layers,activation functions,regularization implementation of convolution neural networks and recurrent neural networks are analyzed.The visualization of convolution kernels is used to further understand the convolution neural networks,which lays a foundation for the improvement of network performance.Secondly,static target recognition is studied and the single frame image is used as input to classify images.This paper mainly focuses on the recognition of traffic signs.This paper first improves the network structure combining a well-trained network model.Then,it improves the loss function according to the collected data distribution characteristics so that the improved model has a higher recognition accuracy.Thirdly,dynamic target recognition is studied and multi-frame images with time information are classified by using multi-frame images as input.This paper mainly focuses on pedestrian behavior recognition.Based on 3D CNNs,combined with the skip-connections,batch normalization and recurrent neural networks,the C3 D model is improved,which makes the improved network model have a higher recognition accuracy.Finally,this paper applies the algorithm in engineering practice,taking the unmanned car as the carrier and the NVIDIA embedded computing device as the computing platform and GPU acceleration to carry out the actual test of the algorithm,which proves that the algorithm can effectively recognize traffic signs and pedestrian behavior in practical application.Through large amount of experiments,it shows that the target recognition algorithm designed in this paper achieves a higher accuracy and has certain theoretical innovation and engineering practicability.