FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring
With the rapid development of the national power grid, there is an increasing and strict demand for accurate intelligent management. However, the current detection algorithms have limited abilities under adverse conditions, especially in regions like Yunnan Province with complex terrain. To address...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2023/5588547 |
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| _version_ | 1832559653645451264 |
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| author | R. Chang B. Zhang Y. Zhang S. Gao S. Zhao Y. Rao X. Zhai T. Wang Y. Yang |
| author_facet | R. Chang B. Zhang Y. Zhang S. Gao S. Zhao Y. Rao X. Zhai T. Wang Y. Yang |
| author_sort | R. Chang |
| collection | DOAJ |
| description | With the rapid development of the national power grid, there is an increasing and strict demand for accurate intelligent management. However, the current detection algorithms have limited abilities under adverse conditions, especially in regions like Yunnan Province with complex terrain. To address this issue, we propose a method that utilizes infrared and visible images to make the images more informative, thereby improving the accuracy of the detection algorithm for electric power construction site safety. First, we design channel attention (CA) module and pixel attention (PA) module to focus on more important channels and resist thick haze pixels that focus on the thick haze pixels and more important channel information. Furthermore, we design a two-stage discriminator which imposes two restrictions on the fused results. Finally, we conduct a large number of comparison experiments with state-of-the-art methods, and the results show that our proposed fusion method achieves excellent performance in infrared and visible image fusion. This method has good prospects for application in the safety supervision of power construction sites and provides a line of defense for construction workers. |
| format | Article |
| id | doaj-art-c4f0a746d35d4e9486e909fac3d67f8a |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-c4f0a746d35d4e9486e909fac3d67f8a2025-02-03T01:29:33ZengWileyAdvances in Multimedia1687-56992023-01-01202310.1155/2023/5588547FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety MonitoringR. Chang0B. Zhang1Y. Zhang2S. Gao3S. Zhao4Y. Rao5X. Zhai6T. Wang7Y. Yang8Yuxi Power Supply BureauYuxi Power Supply BureauYuxi Power Supply BureauGuangzhou Jiansoft Technology Co LtdThe Laboratory of Pattern Recognition and Artificial IntelligenceThe Laboratory of Pattern Recognition and Artificial IntelligenceThe Laboratory of Pattern Recognition and Artificial IntelligenceThe Laboratory of Pattern Recognition and Artificial IntelligenceThe Laboratory of Pattern Recognition and Artificial IntelligenceWith the rapid development of the national power grid, there is an increasing and strict demand for accurate intelligent management. However, the current detection algorithms have limited abilities under adverse conditions, especially in regions like Yunnan Province with complex terrain. To address this issue, we propose a method that utilizes infrared and visible images to make the images more informative, thereby improving the accuracy of the detection algorithm for electric power construction site safety. First, we design channel attention (CA) module and pixel attention (PA) module to focus on more important channels and resist thick haze pixels that focus on the thick haze pixels and more important channel information. Furthermore, we design a two-stage discriminator which imposes two restrictions on the fused results. Finally, we conduct a large number of comparison experiments with state-of-the-art methods, and the results show that our proposed fusion method achieves excellent performance in infrared and visible image fusion. This method has good prospects for application in the safety supervision of power construction sites and provides a line of defense for construction workers.http://dx.doi.org/10.1155/2023/5588547 |
| spellingShingle | R. Chang B. Zhang Y. Zhang S. Gao S. Zhao Y. Rao X. Zhai T. Wang Y. Yang FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring Advances in Multimedia |
| title | FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring |
| title_full | FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring |
| title_fullStr | FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring |
| title_full_unstemmed | FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring |
| title_short | FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring |
| title_sort | ffa gan a generative adversarial network based on feature fusion attention for intelligent safety monitoring |
| url | http://dx.doi.org/10.1155/2023/5588547 |
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