Automatic wildfire monitoring system based on deep learning
Fire detection based on computer vision technology can avoid many flaws in conventional methods. However, existing methods fail to achieve a good trade-off in accuracy, model size, speed, and cost. This paper presents a high-performance forest fire recognition algorithm to solve the current problems...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2133745 |
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| Summary: | Fire detection based on computer vision technology can avoid many flaws in conventional methods. However, existing methods fail to achieve a good trade-off in accuracy, model size, speed, and cost. This paper presents a high-performance forest fire recognition algorithm to solve the current problems in forest fire monitoring. Firstly, visual saliency areas in motion images are extracted to improve detection efficiency. Secondly, transfer learning techniques are employed to improve the generalization performance of the constructed deep learning classification model. Finally, fire detection is realized based on C++ deployment algorithms Compared with the existing forest fire detection methods, the proposed method has higher classification accuracy and speed, with a more comprehensive application range and lower cost. The performance of our method can meet the accuracy and speed requirements of real-time fire detection, and it can be deployed and practiced on multiple platforms. |
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| ISSN: | 2279-7254 |