Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks
A fire is an extraordinary event that can damage property and have a notable effect on people’s lives. However, the early detection of smoke and fire has been identified as a challenge in many recent studies. Therefore, different solutions have been proposed to approach the timely detection of fire...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/1/98 |
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| author | Muksimova Shakhnoza Umirzakova Sabina Mardieva Sevara Young-Im Cho |
| author_facet | Muksimova Shakhnoza Umirzakova Sabina Mardieva Sevara Young-Im Cho |
| author_sort | Muksimova Shakhnoza |
| collection | DOAJ |
| description | A fire is an extraordinary event that can damage property and have a notable effect on people’s lives. However, the early detection of smoke and fire has been identified as a challenge in many recent studies. Therefore, different solutions have been proposed to approach the timely detection of fire events and avoid human casualties. As a solution, we used an affordable visual detection system. This method is possibly effective because early fire detection is recognized. In most developed countries, CCTV surveillance systems are installed in almost every public location to take periodic images of a specific area. Notwithstanding, cameras are used under different types of ambient light, and they experience occlusions, distortions of view, and changes in the resulting images from different camera angles and the different seasons of the year, all of which affect the accuracy of currently established models. To address these problems, we developed an approach based on an attention feature map used in a capsule network designed to classify fire and smoke locations at different distances outdoors, given only an image of a single fire and smoke as input. The proposed model was designed to solve two main limitations of the base capsule network input and the analysis of large-sized images, as well as to compensate the absence of a deep network using an attention-based approach to improve the classification of the fire and smoke results. In term of practicality, our method is comparable with prior strategies based on machine learning and deep learning methods. We trained and tested the proposed model using our datasets collected from different sources. As the results indicate, a high classification accuracy in comparison with other modern architectures was achieved. Further, the results indicate that the proposed approach is robust and stable for the classification of images from outdoor CCTV cameras with different viewpoints given the presence of smoke and fire. |
| first_indexed | 2024-03-10T03:21:56Z |
| format | Article |
| id | doaj.art-42d7b944d9b941928d4b2a173a72a75b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:56Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-42d7b944d9b941928d4b2a173a72a75b2023-11-23T12:16:52ZengMDPI AGSensors1424-82202021-12-012219810.3390/s22010098Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule NetworksMuksimova Shakhnoza0Umirzakova Sabina1Mardieva Sevara2Young-Im Cho3Department of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, KoreaDepartment of IT Convergence Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, KoreaDepartment Information Security, Tashkent University of Information Technologies Named after Muhammad al-Khwarizmi Tashkent, Tashkent 100200, UzbekistanDepartment of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 461-701, Gyeonggi-do, KoreaA fire is an extraordinary event that can damage property and have a notable effect on people’s lives. However, the early detection of smoke and fire has been identified as a challenge in many recent studies. Therefore, different solutions have been proposed to approach the timely detection of fire events and avoid human casualties. As a solution, we used an affordable visual detection system. This method is possibly effective because early fire detection is recognized. In most developed countries, CCTV surveillance systems are installed in almost every public location to take periodic images of a specific area. Notwithstanding, cameras are used under different types of ambient light, and they experience occlusions, distortions of view, and changes in the resulting images from different camera angles and the different seasons of the year, all of which affect the accuracy of currently established models. To address these problems, we developed an approach based on an attention feature map used in a capsule network designed to classify fire and smoke locations at different distances outdoors, given only an image of a single fire and smoke as input. The proposed model was designed to solve two main limitations of the base capsule network input and the analysis of large-sized images, as well as to compensate the absence of a deep network using an attention-based approach to improve the classification of the fire and smoke results. In term of practicality, our method is comparable with prior strategies based on machine learning and deep learning methods. We trained and tested the proposed model using our datasets collected from different sources. As the results indicate, a high classification accuracy in comparison with other modern architectures was achieved. Further, the results indicate that the proposed approach is robust and stable for the classification of images from outdoor CCTV cameras with different viewpoints given the presence of smoke and fire.https://www.mdpi.com/1424-8220/22/1/98capsule networkattention feature mapsmoke detectionfire detectiondeep learningartificial intelligence |
| spellingShingle | Muksimova Shakhnoza Umirzakova Sabina Mardieva Sevara Young-Im Cho Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks Sensors capsule network attention feature map smoke detection fire detection deep learning artificial intelligence |
| title | Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks |
| title_full | Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks |
| title_fullStr | Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks |
| title_full_unstemmed | Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks |
| title_short | Novel Video Surveillance-Based Fire and Smoke Classification Using Attentional Feature Map in Capsule Networks |
| title_sort | novel video surveillance based fire and smoke classification using attentional feature map in capsule networks |
| topic | capsule network attention feature map smoke detection fire detection deep learning artificial intelligence |
| url | https://www.mdpi.com/1424-8220/22/1/98 |
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