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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment.

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Τίτλος: Novel Rifle Number Recognition Based on Improved YOLO in Military Environment.
Συγγραφείς: Hyun Kwon, Sanghyun Lee
Πηγή: Computers, Materials & Continua; 2024, Vol. 78 Issue 1, p249-263, 15p
Θεματικοί όροι: Artificial neural networks, Automatic speech recognition, Object recognition (Computer vision), Rifles, Military supplies, Speech perception
Περίληψη: Deep neural networks perform well in image recognition, object recognition, pattern analysis, and speech recognition. In military applications, deep neural networks can detect equipment and recognize objects. In military equipment, it is necessary to detect and recognize rifle management, which is an important piece of equipment, using deep neural networks. There have been no previous studies on the detection of real rifle numbers using real rifle image datasets. In this study, we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient. The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods. In the proposed method, real rifle images and existing digit images are fusedas trainingdata, andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data (K-2 rifle) and numeric image datasets, as an experimental environment. TensorFlow was used as the machine learning library. Experimental 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 numbers. The proposed method is effective in detecting rifle numbers. [ABSTRACT FROM AUTHOR]
Copyright of Computers, Materials & Continua is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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PubType: Academic Journal
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IllustrationInfo
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  Label: Title
  Group: Ti
  Data: Novel Rifle Number Recognition Based on Improved YOLO in Military Environment.
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  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Hyun+Kwon%22">Hyun Kwon</searchLink><br /><searchLink fieldCode="AR" term="%22Sanghyun+Lee%22">Sanghyun Lee</searchLink>
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  Data: Computers, Materials & Continua; 2024, Vol. 78 Issue 1, p249-263, 15p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Rifles%22">Rifles</searchLink><br /><searchLink fieldCode="DE" term="%22Military+supplies%22">Military supplies</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+perception%22">Speech perception</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Deep neural networks perform well in image recognition, object recognition, pattern analysis, and speech recognition. In military applications, deep neural networks can detect equipment and recognize objects. In military equipment, it is necessary to detect and recognize rifle management, which is an important piece of equipment, using deep neural networks. There have been no previous studies on the detection of real rifle numbers using real rifle image datasets. In this study, we propose a method for detecting and recognizing rifle numbers when rifle image data are insufficient. The proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer learningmethods. In the proposed method, real rifle images and existing digit images are fusedas trainingdata, andthe final layer is transferredto theYolov5 algorithmmodel.The detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical datasets.We used actual rifle image data (K-2 rifle) and numeric image datasets, as an experimental environment. TensorFlow was used as the machine learning library. Experimental 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 numbers. The proposed method is effective in detecting rifle numbers. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Computers, Materials & Continua is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.32604/cmc.2023.042466
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 249
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Automatic speech recognition
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Rifles
        Type: general
      – SubjectFull: Military supplies
        Type: general
      – SubjectFull: Speech perception
        Type: general
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            NameFull: Sanghyun Lee
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              M: 01
              Text: 2024
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              Y: 2024
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