Research On Infrared Scanning Target Recognition Technology

Infrared scanning target recognition technology has a wide range of applications in military,security,intelligent driving and many other fields.On the one hand,it is difficult for most current infrared imaging devices to obtain infrared images with a wide range,wide viewing angle,and high resolution.on the other hand,there is a problem of high false alarm rate in identifying small targets in complex infrared scenes.Therefore,the research on infrared scanning target recognition technology has important practical significance.This paper focuses on the research of infrared splicing technology and infrared target recognition technology in the all-round mode.On this basis,the infrared scanning target recognition software is designed and implemented.In the aspect of image preprocessing,this paper analyzes the imaging principles and characteristics of infrared images.Based on the above analysis,several classical image filtering methods and image enhancement methods are introduced.Through experimental comparison and analysis,the median filtering method and the image enhancement method based on Laplacian operator are finally adopted as the image preprocessing method.In terms of infrared target recognition,this paper introduces several types of infrared target detection methods based on deep learning.In order to ensure the balance between real-time performance and accuracy in practical application scenarios,the YOLO v3 is selected for improvement and optimization.In view of the fact that the target scale is mostly concentrated in the small range,the feature scale is increased to strengthen the detection of small targets;at the same time,the feature fusion module is added to increase the weight of small features;finally,the loss function is improved to increase the accuracy.In this paper,the self-collected and annotated infrared UAV dataset is used for experimental verification.The experimental results show that the improved algorithm improves the detection accuracy of small objects by 7% compared with the benchmark framework.In terms of infrared image stitching,current infrared images have the problem of narrow field of view and low resolution,and stitching with SIFT and SUFT algorithms spends a long time,a real-time infrared large-field stitching algorithm based on fast feature extraction and description(ORB)is proposed.The algorithm makes full use of the prior information of the positional relationship between images,adopts the registration method based on the bidirectional matching strategy.Finally,an adaptive weighted fusion algorithm is introduced.On the basis of ensuring the quality of image stitching and improving the efficiency of image stitching,fast and accurate infrared large-field image stitching can be achieved.The experimental results show that under the same level of matching rate,the time-consuming of the improved algorithm is reduced by 40.5% compared with the traditional ORB algorithm.Based on the above research,using infrared large field of view splicing and target recognition in infrared images,supplemented with video storage,alarm generation,human-computer interaction,image manipulation and other application functions,this paper designs an infrared scanning target recognition software.The experimental demonstration results of the software show that the software can not only realize allround infrared imaging,but also detect and identify key targets.