Research On Aircraft Target Recognition Algorithm Based On Convolutional Neural Network

With the development of social economy,our country has also made further breakthroughs in the field of aviation.The types and numbers of aircraft are constantly increasing,resulting in a state of near saturation in many airspaces in China.Combined with the existing problems,study various possible collision scenarios in the air and formulate a risk avoidance plan.Among them,the unmanned aerial vehicle voluntarily completes the recognition of the flying target plays an important role.Since this time,because of the rapid development of society,the concept of deep learning has also entered a new stage,so we can see the shadow of deep learning in many scenarios.For this article,research on the application of convolutional neural networks in aircraft image recognition is essential.In recent years,aircraft target recognition has also made some preliminary attempts using deep learning.This paper studies aircraft recognition methods based on the TensorFlow learning framework.Aircraft recognition is divided into two major sections to focus on research,candidate area generation algorithm and image classification algorithm.First,it introduces the basic theoretical algorithm convolutional neural network throughout the whole text,introduces its various levels of structure,function and advantages,and selects the deep learning framework used in this article.Secondly,it introduces and analyzes several types of algorithms used in the structure of candidate region generation algorithms,and draws the advantages and disadvantages of these algorithms.The candidate region generation algorithm draws on the ideas of the faster R-CNN algorithm and proposes an improved candidate region generation algorithm.Then,introduce several algorithms for image classification.And based on the AlexNet network to improve the network,combined with the probabilistic neural network to obtain a new model with higher accuracy,and use data enhancement methods to solve data shortage problems.Finally,the two algorithms are merged,and the pooling layer of interest is added to adjust the output feature layer size.The feasibility of this algorithm was verified through comparative simulation,and the simulation tests showed that the classification accuracy of the aircraft reached more than 90%,and provided a new idea for the development of aircraft targets recognition.