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

被引:0
|
作者
Aguilar, Ivan Gaytan [1 ]
Contreras, Alejandro Aguilar [1 ]
Eleuterio, Roberto Alejo [1 ]
Lara, Erendira Rendon [1 ]
Pina, Grisel Miranda [1 ]
Gutierrez, Everardo E. Granda [2 ]
机构
[1] Tecnol Nacl Mexico, Campus Toluca, Metepec, Mexico
[2] Univ Autonoma Estado Mexico, Toluca De Lerdo, Mexico
关键词
artificial intelligence; deep learning; violence against women; transfer learning; CNN; VGG16; ResNet50; Mobile Net; IMPACT;
D O I
暂无
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
This paper presents a performance comparison between 3 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, 2800 images (1400 violent and 1400 non-violent) were collected from a public dataset and subsequently divided into training (1200 images), validation (1000 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.
引用
收藏
页数:1
相关论文
共 50 条
  • [41] Adaptive exploitation of pre-trained deep convolutional neural networks for robust visual tracking
    Marvasti-Zadeh, Seyed Mojtaba
    Ghanei-Yakhdan, Hossein
    Kasaei, Shohreh
    MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (14) : 22027 - 22076
  • [42] Comparative Analysis of Pre-trained Deep Neural Networks for Plant Disease Classification
    George, Romiyal
    Thuseethan, Selvarajah
    Ragel, Roshan G.
    2024 21ST INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING, JCSSE 2024, 2024, : 179 - 186
  • [43] An Experimental Study on Fundamental Frequency Detection in Reverberated Speech with Pre-trained Recurrent Neural Networks
    Alfaro-Picado, Andrei
    Solis-Cerdas, Stacy
    Coto-Jimenez, Marvin
    HIGH PERFORMANCE COMPUTING, CARLA 2019, 2020, 1087 : 355 - 368
  • [44] Painting Classification Using a Pre-trained Convolutional Neural Network
    Banerji, Sugata
    Sinha, Atreyee
    COMPUTER VISION, GRAPHICS, AND IMAGE PROCESSING, ICVGIP 2016, 2017, 10481 : 168 - 179
  • [45] Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks
    Kieffer, Brady
    Babaie, Morteza
    Kalra, Shivam
    Tizhoosh, H. R.
    PROCEEDINGS OF THE 2017 SEVENTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA 2017), 2017,
  • [46] CLASSIFICATION OF NOISE BETWEEN FLOORS IN A BUILDING USING PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORKS
    Choi, Hwiyong
    Lee, Seungjun
    Yang, Haesang
    Seong, Woojae
    2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), 2018, : 535 - 539
  • [47] Teaming Up Pre-Trained Deep Neural Networks
    Deabes, Wael
    Abdel-Hakim, Alaa E.
    2018 INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INFORMATION SECURITY (ICSPIS), 2018, : 73 - 76
  • [48] Epistemic Uncertainty Quantification For Pre-trained Neural Networks
    Wang, Hanjing
    Ji, Qiang
    2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2024, : 11052 - 11061
  • [49] Hyperparameter optimization of pre-trained convolutional neural networks using adolescent identity search algorithm
    Ebubekir Akkuş
    Ufuk Bal
    Fatma Önay Koçoğlu
    Selami Beyhan
    Neural Computing and Applications, 2024, 36 : 1523 - 1537
  • [50] Budget Restricted Incremental Learning with Pre-Trained Convolutional Neural Networks and Binary Associative Memories
    Hacene, Ghouthi Boukli
    Gripon, Vincent
    Farrugia, Nicolas
    Arzel, Matthieu
    Jezequel, Michel
    2017 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS), 2017,