Lightweight Convolutional Neural Network Model for Human Face Detection in Risk Situations
In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2022-07, Vol.18 (7), p.4820-4829 |
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| container_title | IEEE transactions on industrial informatics |
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| creator | Wieczorek, Michal Silka, Jakub Wozniak, Marcin Garg, Sahil Hassan, Mohammad Mehedi |
| description | In this article, we propose a model of face detection in risk situations to help rescue teams speed up the search of people who might need help. The proposed lightweight convolutional neural network (CNN) architecture is designed to detect faces of people in mines, avalanches, under water, or other dangerous situations when their face might not be very visible over surrounding background. We have designed a novel light architecture cooperating with the proposed sliding window procedure. The designed model works with maximum simplicity to support mobile devices. An output from processing presents a box on face location in the screen of device. The model was trained by using Adam and tested on various images. Results show that proposed lightweight CNN detects human faces over various textures with accuracy above 99% and precision above 98% what proves the efficiency of our proposed model. |
| doi_str_mv | 10.1109/TII.2021.3129629 |
| format | Article |
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| subjects | Artificial neural networks Computational modeling Convolutional neural networks Deep learning Electronic devices Face detection Face recognition Faces Informatics Lightweight lightweight convolutional neural network (CNN) Mobile handsets Neural networks |
| title | Lightweight Convolutional Neural Network Model for Human Face Detection in Risk Situations |
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