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A Comparative Study of Three Pre-trained Convolutional Neural Networks in the Detection of Violence Against Women

  • Autores: Ivan Gaytán Aguilar, Alejandro Aguilar, Roberto Alejo Eleuterio Árbol académico, Eréndira Rendón Lara, Grisel Miranda Piña, Everardo E. Granda Gutiérrez
  • Localización: CIENCIA ergo-sum, ISSN 1405-0269, Vol. 31, Nº. 1, 2024
  • Idioma: inglés
  • DOI: 10.30878/ces.v31n0a17
  • Títulos paralelos:
    • Estudio comparativo de tres redes neuronales convolucionales preentrenadas en la detección de violencia contra las mujeres
  • Enlaces
  • Resumen
    • español

      Se presenta una comparación de rendimiento entre tres modelos de redes CNN preentrenadas (VGG16, ResNet50 y MobileNet) en la detección en video de violencia física contra la mujer. Para llevar a cabo la clasificación de imágenes que incluyan violencia física contra la mujer y aquellas que no, se recolectaron 2 800 imágenes (1 400 violentas y 1 400 no violentas) de un Dataset público y posteriormente fueron divididas en entrenamiento (1 200 imágenes), validación (1 000 imágenes) y prueba (600 imágenes). Para evaluar su rendimiento, se tomaron en cuenta los valores de exactitud para cada modelo; al respecto, la red MobileNet se posiciona como el clasificador con mejor rendimiento para esta tarea de clasificación con 89% de exactitud.

    • English

      This paper presents a performance comparison between three models of pre-trained CNN networks (VGG16, ResNet50, and MobileNet) in detecting physical violence against women in video. To carry out the classification of images that include physical violence against women and those that do not, 2 800 images (1 400 violent and 1 400 non-violent) were collected from a public dataset and subsequently divided into training (1 200 images), validation (1 000 images) and test (600 images). To evaluate their performance, accuracy values for each model were considered, positioning the MobileNet network as the best-performing classifier for this classification task with 89% accuracy

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