Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

作     者：Hyun Kwon Sanghyun Lee 

作者机构：Department of Artificial Intelligence and Data ScienceKorea Military AcademySeoulKorea Graduate School of Information SecurityKorea Advanced Institute of Science and TechnologyDaejeonKorea 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第78卷第1期

页      面：249-263页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the Future Strategy and Technology Research Institute(RN:23-AI-04)of Korea Military Academy the Hwarang-Dae Research Institute(RN:2023B1015)of Korea Military Academy,and Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2021R1I1A1A01040308) 

主　　题：Machine learning deep neural network rifle number recognition detection 

摘      要：Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.