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Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model


  • Elhoucine Ouassam Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
  • Yassine Dabachine Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
  • Nabil Hmina Laboratory LIMATI, Department of Mathematics and Informatics, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
  • Belaid Bouikhalene 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


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