Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation
Fire in power equipment has always been one of the main hazards of power equipment. Smoke detection and recognition have always been extremely important in power equipment, as they can provide early warning before a fire breaks out. Compared to relying on smoke concentration for recognition, image-b...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/9298478 |
| _version_ | 1797351730912952320 |
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| author | Rong Chang Zhengxiong Mao Jian Hu Haicheng Bai Anning Pan Yang Yang Shan Gao |
| author_facet | Rong Chang Zhengxiong Mao Jian Hu Haicheng Bai Anning Pan Yang Yang Shan Gao |
| author_sort | Rong Chang |
| collection | DOAJ |
| description | Fire in power equipment has always been one of the main hazards of power equipment. Smoke detection and recognition have always been extremely important in power equipment, as they can provide early warning before a fire breaks out. Compared to relying on smoke concentration for recognition, image-based smoke recognition has the advantage of being unaffected by indoor and outdoor environments. This paper addresses the problems of limited smoke data, difficult labeling, and insufficient research on recognition algorithms in power systems. We propose using three-dimensional virtual technology to generate smoke and image masks and using environmental backgrounds such as HDR (high dynamic range imaging) lighting to realistically combine smoke and background. In addition, to address the characteristics of smoke in power equipment, a dual UNet model named DS-UNet is proposed. The model consists of a deep and a shallow network structure, which can effectively segment the details of smoke in power equipment and handle partial occlusion. Finally, DS-UNet is compared with other smoke segmentation networks with similar structures, and it demonstrates better smoke segmentation performance. |
| first_indexed | 2024-03-08T13:04:48Z |
| format | Article |
| id | doaj.art-f82e716749644c8cb7b83a6c972813fa |
| institution | Directory Open Access Journal |
| issn | 2090-0155 |
| language | English |
| last_indexed | 2024-03-08T13:04:48Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj.art-f82e716749644c8cb7b83a6c972813fa2024-01-19T00:00:02ZengHindawi LimitedJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/9298478Generation of Smoke Dataset for Power Equipment and Study of Image Semantic SegmentationRong Chang0Zhengxiong Mao1Jian Hu2Haicheng Bai3Anning Pan4Yang Yang5Shan Gao6Yuxi Power Supply BureauInformation CenterInformation CenterNetwork and Information CenterSchool of Information Science and TechnologySchool of Information Science and TechnologyGuangzhou JianRuan Technology Co., Ltd.Fire in power equipment has always been one of the main hazards of power equipment. Smoke detection and recognition have always been extremely important in power equipment, as they can provide early warning before a fire breaks out. Compared to relying on smoke concentration for recognition, image-based smoke recognition has the advantage of being unaffected by indoor and outdoor environments. This paper addresses the problems of limited smoke data, difficult labeling, and insufficient research on recognition algorithms in power systems. We propose using three-dimensional virtual technology to generate smoke and image masks and using environmental backgrounds such as HDR (high dynamic range imaging) lighting to realistically combine smoke and background. In addition, to address the characteristics of smoke in power equipment, a dual UNet model named DS-UNet is proposed. The model consists of a deep and a shallow network structure, which can effectively segment the details of smoke in power equipment and handle partial occlusion. Finally, DS-UNet is compared with other smoke segmentation networks with similar structures, and it demonstrates better smoke segmentation performance.http://dx.doi.org/10.1155/2024/9298478 |
| spellingShingle | Rong Chang Zhengxiong Mao Jian Hu Haicheng Bai Anning Pan Yang Yang Shan Gao Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation Journal of Electrical and Computer Engineering |
| title | Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation |
| title_full | Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation |
| title_fullStr | Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation |
| title_full_unstemmed | Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation |
| title_short | Generation of Smoke Dataset for Power Equipment and Study of Image Semantic Segmentation |
| title_sort | generation of smoke dataset for power equipment and study of image semantic segmentation |
| url | http://dx.doi.org/10.1155/2024/9298478 |
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