FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5
Intense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this paper considers the classification and identifi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/12/2129 |
| _version_ | 1797458356135264256 |
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| author | Qilin Xue Haifeng Lin Fang Wang |
| author_facet | Qilin Xue Haifeng Lin Fang Wang |
| author_sort | Qilin Xue |
| collection | DOAJ |
| description | Intense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this paper considers the classification and identification of surface fires and canopy fires. Deep learning-based forest fire detection uses convolutional neural networks to automatically extract multidimensional features of forest fire images with high detection accuracy. To accurately identify different forest fire types in complex backgrounds, an improved forest fire classification and detection model (FCDM) based on YOLOv5 is presented in this paper, which uses image-based data. By changing the YOLOv5 bounding box loss function to SIoU Loss and introducing directionality in the cost of the loss function to achieve faster convergence, the training and inference of the detection algorithm are greatly improved. The Convolutional Block Attention Module (CBAM) is introduced in the network to fuse channel attention and spatial attention to improve the classification recognition accuracy. The Path Aggregation Network (PANet) layer in the YOLOv5 algorithm is improved into a weighted Bi-directional Feature Pyramid Network (BiFPN) to fuse and filter forest fire features of different dimensions to improve the detection of different types of forest fires. The experimental results show that this improved forest fire classification and identification model outperforms the YOLOv5 algorithm in both detection performances. The mAP@0.5 of fire detection, surface fire detection, and canopy fire detection was improved by 3.9%, 4.0%, and 3.8%, respectively. Among them, the mAP@0.5 of surface fire reached 83.1%, and the canopy fire detection reached 90.6%. This indicates that the performance of our proposed improved model has been effectively improved and has some application prospects in forest fire classification and recognition. |
| first_indexed | 2024-03-09T16:36:53Z |
| format | Article |
| id | doaj.art-146c3971542743d39983c6066ea82d6b |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-09T16:36:53Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-146c3971542743d39983c6066ea82d6b2023-11-24T14:55:57ZengMDPI AGForests1999-49072022-12-011312212910.3390/f13122129FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5Qilin Xue0Haifeng Lin1Fang Wang2College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Electronic Engineering, Nanjing XiaoZhuang University, Nanjing 211171, ChinaIntense, large-scale forest fires are damaging and very challenging to control. Locations, where various types of fire behavior occur, vary depending on environmental factors. According to the burning site of forest fires and the degree of damage, this paper considers the classification and identification of surface fires and canopy fires. Deep learning-based forest fire detection uses convolutional neural networks to automatically extract multidimensional features of forest fire images with high detection accuracy. To accurately identify different forest fire types in complex backgrounds, an improved forest fire classification and detection model (FCDM) based on YOLOv5 is presented in this paper, which uses image-based data. By changing the YOLOv5 bounding box loss function to SIoU Loss and introducing directionality in the cost of the loss function to achieve faster convergence, the training and inference of the detection algorithm are greatly improved. The Convolutional Block Attention Module (CBAM) is introduced in the network to fuse channel attention and spatial attention to improve the classification recognition accuracy. The Path Aggregation Network (PANet) layer in the YOLOv5 algorithm is improved into a weighted Bi-directional Feature Pyramid Network (BiFPN) to fuse and filter forest fire features of different dimensions to improve the detection of different types of forest fires. The experimental results show that this improved forest fire classification and identification model outperforms the YOLOv5 algorithm in both detection performances. The mAP@0.5 of fire detection, surface fire detection, and canopy fire detection was improved by 3.9%, 4.0%, and 3.8%, respectively. Among them, the mAP@0.5 of surface fire reached 83.1%, and the canopy fire detection reached 90.6%. This indicates that the performance of our proposed improved model has been effectively improved and has some application prospects in forest fire classification and recognition.https://www.mdpi.com/1999-4907/13/12/2129forest fire classification detectionYOLOv5SIoU LossCBAMBiFPN |
| spellingShingle | Qilin Xue Haifeng Lin Fang Wang FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5 Forests forest fire classification detection YOLOv5 SIoU Loss CBAM BiFPN |
| title | FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5 |
| title_full | FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5 |
| title_fullStr | FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5 |
| title_full_unstemmed | FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5 |
| title_short | FCDM: An Improved Forest Fire Classification and Detection Model Based on YOLOv5 |
| title_sort | fcdm an improved forest fire classification and detection model based on yolov5 |
| topic | forest fire classification detection YOLOv5 SIoU Loss CBAM BiFPN |
| url | https://www.mdpi.com/1999-4907/13/12/2129 |
| work_keys_str_mv | AT qilinxue fcdmanimprovedforestfireclassificationanddetectionmodelbasedonyolov5 AT haifenglin fcdmanimprovedforestfireclassificationanddetectionmodelbasedonyolov5 AT fangwang fcdmanimprovedforestfireclassificationanddetectionmodelbasedonyolov5 |