Deep Learning-Based Fire Detection for Enhanced Safety Systems
Fire detection systems are a critical aspect of modern safety and security systems, playing a pivotal role in safeguarding lives and property against the destructive force of fires. Rapid and accurate identification of fire incidents is essential for timely response and mitigation efforts. Traditio...
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| Format: | Article |
| Language: | English |
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College of Education for Pure Sciences
2023-12-01
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| Series: | Wasit Journal for Pure Sciences |
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| Online Access: | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/221 |
| _version_ | 1797288423924432896 |
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| author | Mothefer Majeed Jahefer |
| author_facet | Mothefer Majeed Jahefer |
| author_sort | Mothefer Majeed Jahefer |
| collection | DOAJ |
| description |
Fire detection systems are a critical aspect of modern safety and security systems, playing a pivotal role in safeguarding lives and property against the destructive force of fires. Rapid and accurate identification of fire incidents is essential for timely response and mitigation efforts. Traditional fire detection methods have made substantial advancements, but with the advent of computer vision technologies, the field has witnessed a transformative shift. This paper presents a method for fire detection using deep convolutional neural network (CNN) models. This approach used transfer learning by employing two pre-trained CNN models from the ImageNet dataset: VGG (Visual Geometry Group) and InceptionV3 to extract valuable features from input images. Then, these extracted features serve as input for a machine learning (ML) classifier, namely the Softmax classifier. The Softmax activation function computes the probability distribution to assign accurate class probabilities for discriminating between two types of images: fire and non-fire. Experimental results showed that the proposed method successfully detected fire areas and achieved seamless classification performance compared to other current fire detection methods.
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| first_indexed | 2024-03-07T18:48:10Z |
| format | Article |
| id | doaj.art-3b2a6a40ae3145aaaf0a33ea928bc7df |
| institution | Directory Open Access Journal |
| issn | 2790-5233 2790-5241 |
| language | English |
| last_indexed | 2024-03-07T18:48:10Z |
| publishDate | 2023-12-01 |
| publisher | College of Education for Pure Sciences |
| record_format | Article |
| series | Wasit Journal for Pure Sciences |
| spelling | doaj.art-3b2a6a40ae3145aaaf0a33ea928bc7df2024-03-02T02:02:35ZengCollege of Education for Pure SciencesWasit Journal for Pure Sciences2790-52332790-52412023-12-012410.31185/wjps.221Deep Learning-Based Fire Detection for Enhanced Safety SystemsMothefer Majeed Jahefer0Department of Computer Techniques Engineering, Imam Al-Kadhum College (IKC), Baghdad, Iraq Fire detection systems are a critical aspect of modern safety and security systems, playing a pivotal role in safeguarding lives and property against the destructive force of fires. Rapid and accurate identification of fire incidents is essential for timely response and mitigation efforts. Traditional fire detection methods have made substantial advancements, but with the advent of computer vision technologies, the field has witnessed a transformative shift. This paper presents a method for fire detection using deep convolutional neural network (CNN) models. This approach used transfer learning by employing two pre-trained CNN models from the ImageNet dataset: VGG (Visual Geometry Group) and InceptionV3 to extract valuable features from input images. Then, these extracted features serve as input for a machine learning (ML) classifier, namely the Softmax classifier. The Softmax activation function computes the probability distribution to assign accurate class probabilities for discriminating between two types of images: fire and non-fire. Experimental results showed that the proposed method successfully detected fire areas and achieved seamless classification performance compared to other current fire detection methods. https://wjps.uowasit.edu.iq/index.php/wjps/article/view/221Fire detectionDeep learning, CNNVGG16InceptionV3 |
| spellingShingle | Mothefer Majeed Jahefer Deep Learning-Based Fire Detection for Enhanced Safety Systems Wasit Journal for Pure Sciences Fire detection Deep learning , CNN VGG16 InceptionV3 |
| title | Deep Learning-Based Fire Detection for Enhanced Safety Systems |
| title_full | Deep Learning-Based Fire Detection for Enhanced Safety Systems |
| title_fullStr | Deep Learning-Based Fire Detection for Enhanced Safety Systems |
| title_full_unstemmed | Deep Learning-Based Fire Detection for Enhanced Safety Systems |
| title_short | Deep Learning-Based Fire Detection for Enhanced Safety Systems |
| title_sort | deep learning based fire detection for enhanced safety systems |
| topic | Fire detection Deep learning , CNN VGG16 InceptionV3 |
| url | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/221 |
| work_keys_str_mv | AT mothefermajeedjahefer deeplearningbasedfiredetectionforenhancedsafetysystems |