Early smoke detection of forest fires based on SVM image segmentation
A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Czech Academy of Agricultural Sciences
2019-04-01
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| Series: | Journal of Forest Science |
| Subjects: | |
| Online Access: | https://jfs.agriculturejournals.cz/artkey/jfs-201904-0004_early-smoke-detection-of-forest-fires-based-on-svm-image-segmentation.php |
| _version_ | 1797898705653727232 |
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| author | Ding Xiong Lu Yan |
| author_facet | Ding Xiong Lu Yan |
| author_sort | Ding Xiong |
| collection | DOAJ |
| description | A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection. |
| first_indexed | 2024-04-10T08:17:22Z |
| format | Article |
| id | doaj.art-69a77fe05cf749dca733ee2a9d7604a8 |
| institution | Directory Open Access Journal |
| issn | 1212-4834 1805-935X |
| language | English |
| last_indexed | 2024-04-10T08:17:22Z |
| publishDate | 2019-04-01 |
| publisher | Czech Academy of Agricultural Sciences |
| record_format | Article |
| series | Journal of Forest Science |
| spelling | doaj.art-69a77fe05cf749dca733ee2a9d7604a82023-02-23T03:43:01ZengCzech Academy of Agricultural SciencesJournal of Forest Science1212-48341805-935X2019-04-0165415015910.17221/82/2018-JFSjfs-201904-0004Early smoke detection of forest fires based on SVM image segmentationDing XiongLu Yan0School of Electrical and Information Engineering, Hunan International Economics University, Changsha, ChinaA smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.https://jfs.agriculturejournals.cz/artkey/jfs-201904-0004_early-smoke-detection-of-forest-fires-based-on-svm-image-segmentation.phpsupport vector machines (svm)single framehorizon detectionsuperpixel mergingforest fire prevention |
| spellingShingle | Ding Xiong Lu Yan Early smoke detection of forest fires based on SVM image segmentation Journal of Forest Science support vector machines (svm) single frame horizon detection superpixel merging forest fire prevention |
| title | Early smoke detection of forest fires based on SVM image segmentation |
| title_full | Early smoke detection of forest fires based on SVM image segmentation |
| title_fullStr | Early smoke detection of forest fires based on SVM image segmentation |
| title_full_unstemmed | Early smoke detection of forest fires based on SVM image segmentation |
| title_short | Early smoke detection of forest fires based on SVM image segmentation |
| title_sort | early smoke detection of forest fires based on svm image segmentation |
| topic | support vector machines (svm) single frame horizon detection superpixel merging forest fire prevention |
| url | https://jfs.agriculturejournals.cz/artkey/jfs-201904-0004_early-smoke-detection-of-forest-fires-based-on-svm-image-segmentation.php |
| work_keys_str_mv | AT dingxiong earlysmokedetectionofforestfiresbasedonsvmimagesegmentation AT luyan earlysmokedetectionofforestfiresbasedonsvmimagesegmentation |