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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Child face detection on front passenger seat through deep learning.

Carlos Hernández-Aguilar1, José A Aguilar-Saguilan1, Alejandro I Trejo-Castro2

  • 1Escuela de Ingeniería y Tecnologías, Universidad de Monterrey, San Pedro Garza García, México.

Traffic Injury Prevention
|May 8, 2024
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Summary
This summary is machine-generated.

Car crashes are a leading cause of death for young people. A new child face detection system alerts drivers if a child is in the front seat, preventing fatalities from airbag deployment.

Keywords:
Child safetydeep learningface detectionpassenger seatvehicle safety

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Automotive Safety

Background:

  • Road traffic accidents are a major cause of mortality among young individuals globally.
  • Airbag deployment in frontal collisions poses a lethal risk to children seated in the front passenger seat, particularly those under 13 years of age.

Purpose of the Study:

  • To develop and evaluate an interior monitoring system utilizing child face detection to mitigate the risk of child fatalities in car accidents.
  • To raise driver awareness regarding the danger of allowing children to occupy the front passenger seat.

Main Methods:

  • The system employs deep learning techniques, including transfer learning, fine-tuning, and facial detection, for robust child identification.
  • A custom dataset was created for training, and the MobileNetV2 architecture was selected for its performance and low computational cost, enabling implementation on a Raspberry Pi 4B.
  • Data augmentation techniques were used to expand the dataset, resulting in 2,496 adult and 2,310 child images.

Main Results:

  • The system achieved 98% accuracy and 100% precision in face classification without a sliding window.
  • Real-time detection of children in the front passenger seat was accomplished with a 1-second delay per decision, reaching 100% accuracy.
  • The developed system demonstrated robust performance in identifying children in the front seat.

Conclusions:

  • The study demonstrates the feasibility of implementing a robust, non-invasive child detection system in automobiles using deep learning on a Raspberry Pi 4 Model B.
  • While experimental accuracy was 100%, real-world conditions like sunlight and debris may affect performance.
  • The system offers a potential solution for enhancing child safety in vehicles by preventing front-seat placement.