Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach
Fire detection technologies remain a critical component of building automation. With the recent significant advances in computer vision, visual fire detection methods have been developed and integrated into building surveillance systems. Overfitting and accuracy challenges remain in fire detection w...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9309261/ |
| _version_ | 1818639914042916864 |
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| author | Zhaoyi Xu Yanjie Guo Joseph Homer Saleh |
| author_facet | Zhaoyi Xu Yanjie Guo Joseph Homer Saleh |
| author_sort | Zhaoyi Xu |
| collection | DOAJ |
| description | Fire detection technologies remain a critical component of building automation. With the recent significant advances in computer vision, visual fire detection methods have been developed and integrated into building surveillance systems. Overfitting and accuracy challenges remain in fire detection when training datasets are limited. In this work, we tackle these challenges by developing a deep convolutional generative adversarial network (DCGAN) for highly accurate visual fire detection when training images are limited. Our model addresses three types of errors in visual fire detection with small training datasets: model overfitting, fire probability overestimation, and fire probability underestimation. The DCGAN includes a generator of fake fire images for self-supervised learning (SSL) and a discriminator for image classification. We designed computational experiments with high-quality datasets to test and validate our model against other supervised learning approaches. We also benchmarked the performance of the DCGAN against a best-in-class deep visual fire detection model. The results show that our model significantly outperforms other fire detection models on all performance metrics when trained with the same small dataset. The results demonstrate that the DCGAN effectively mitigates the three types of error when the training dataset is limited. |
| first_indexed | 2024-12-16T23:02:56Z |
| format | Article |
| id | doaj.art-49cfe9e1d8e64ab5986a12d7e8328c60 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T23:02:56Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-49cfe9e1d8e64ab5986a12d7e8328c602022-12-21T22:12:39ZengIEEEIEEE Access2169-35362021-01-0193936394610.1109/ACCESS.2020.30477649309261Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network ApproachZhaoyi Xu0https://orcid.org/0000-0002-8498-3483Yanjie Guo1https://orcid.org/0000-0002-1115-7383Joseph Homer Saleh2https://orcid.org/0000-0001-7590-9399School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USASchool of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USAFire detection technologies remain a critical component of building automation. With the recent significant advances in computer vision, visual fire detection methods have been developed and integrated into building surveillance systems. Overfitting and accuracy challenges remain in fire detection when training datasets are limited. In this work, we tackle these challenges by developing a deep convolutional generative adversarial network (DCGAN) for highly accurate visual fire detection when training images are limited. Our model addresses three types of errors in visual fire detection with small training datasets: model overfitting, fire probability overestimation, and fire probability underestimation. The DCGAN includes a generator of fake fire images for self-supervised learning (SSL) and a discriminator for image classification. We designed computational experiments with high-quality datasets to test and validate our model against other supervised learning approaches. We also benchmarked the performance of the DCGAN against a best-in-class deep visual fire detection model. The results show that our model significantly outperforms other fire detection models on all performance metrics when trained with the same small dataset. The results demonstrate that the DCGAN effectively mitigates the three types of error when the training dataset is limited.https://ieeexplore.ieee.org/document/9309261/Deep convolutional generative adversarial networkself-supervised learningvisual fire detection |
| spellingShingle | Zhaoyi Xu Yanjie Guo Joseph Homer Saleh Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach IEEE Access Deep convolutional generative adversarial network self-supervised learning visual fire detection |
| title | Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach |
| title_full | Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach |
| title_fullStr | Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach |
| title_full_unstemmed | Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach |
| title_short | Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach |
| title_sort | tackling small data challenges in visual fire detection a deep convolutional generative adversarial network approach |
| topic | Deep convolutional generative adversarial network self-supervised learning visual fire detection |
| url | https://ieeexplore.ieee.org/document/9309261/ |
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