Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model
The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and auto...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | Arabic |
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College of Science for Women, University of Baghdad
2024-02-01
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| Series: | Baghdad Science Journal |
| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8546 |
| _version_ | 1797324007625719808 |
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| author | Elhoucine Ouassam Yassine Dabachine Nabil Hmina Belaid Bouikhalene |
| author_facet | Elhoucine Ouassam Yassine Dabachine Nabil Hmina Belaid Bouikhalene |
| author_sort | Elhoucine Ouassam |
| collection | DOAJ |
| description |
The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow management at international airports. The implementation of this method has shown superior performance to previous methods in terms of reducing errors, delays and associated costs
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| first_indexed | 2024-03-08T05:36:15Z |
| format | Article |
| id | doaj.art-c92ba3ea0fb44af58af6c14c3d918002 |
| institution | Directory Open Access Journal |
| issn | 2078-8665 2411-7986 |
| language | Arabic |
| last_indexed | 2024-03-08T05:36:15Z |
| publishDate | 2024-02-01 |
| publisher | College of Science for Women, University of Baghdad |
| record_format | Article |
| series | Baghdad Science Journal |
| spelling | doaj.art-c92ba3ea0fb44af58af6c14c3d9180022024-02-05T20:05:17ZaraCollege of Science for Women, University of BaghdadBaghdad Science Journal2078-86652411-79862024-02-0121210.21123/bsj.2023.8546Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection modelElhoucine Ouassam 0Yassine Dabachine 1Nabil Hmina2Belaid Bouikhalene3Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow management at international airports. The implementation of this method has shown superior performance to previous methods in terms of reducing errors, delays and associated costs https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8546 |
| spellingShingle | Elhoucine Ouassam Yassine Dabachine Nabil Hmina Belaid Bouikhalene Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model Baghdad Science Journal |
| title | Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model |
| title_full | Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model |
| title_fullStr | Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model |
| title_full_unstemmed | Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model |
| title_short | Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model |
| title_sort | improving the efficiency and security of passport control processes at airports by using the r cnn object detection model |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/8546 |
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