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Article

A Forest Fire Identification System Based on Weighted Fusion Algorithm

College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
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Forests 2022, 13(8), 1301; https://doi.org/10.3390/f13081301
Submission received: 2 July 2022 / Revised: 28 July 2022 / Accepted: 12 August 2022 / Published: 16 August 2022
(This article belongs to the Section Natural Hazards and Risk Management)

Abstract

The occurrence of forest fires causes serious damage to ecological diversity and the safety of people’s property and life. However, due to the complex forest environment, the changeable shape of forest fires, and the uncertainty of flame color and texture, forest fire detection becomes very difficult. Traditional image processing methods rely heavily on artificial features and are not generally applicable to different forest fire scenes. In order to solve the problem of inaccurate forest fire recognition caused by the manual extraction of features, some scholars use deep learning technology to adaptively learn and extract forest fire features, but they often use a single target detection model, and their lack of learning and perception makes it difficult for them to accurately identify forest fires in a complex forest fire environment. Therefore, in order to overcome the shortcomings of the manual extraction of features and achieve a higher accuracy of forest fire recognition, this paper proposes an algorithm based on weighted fusion to identify forest fire sources in different scenarios, fuses two independent weakly supervised models Yolov5 and EfficientDet, completes the training and prediction of data sets in parallel, and uses the weighted boxes fusion algorithm (WBF) to process the prediction results to obtain the fusion frame. Finally, the model is evaluated by Microsoft COCO standard. Experimental results show that compared with Yolov5 and EfficientDet, the proposed Y4SED improves the detection performance by 2.5% to 4.5%. The fused algorithm proposed in this paper has better feature extraction ability, can extract more forest fire feature information, and better balances the recognition accuracy and complexity of the model, which provides a reference for forest fire target detection in the real environment.
Keywords: forest fire identification; feature extraction; integrated learning; deep learning; artificial intelligence forest fire identification; feature extraction; integrated learning; deep learning; artificial intelligence

Share and Cite

MDPI and ACS Style

Qian, J.; Lin, H. A Forest Fire Identification System Based on Weighted Fusion Algorithm. Forests 2022, 13, 1301. https://doi.org/10.3390/f13081301

AMA Style

Qian J, Lin H. A Forest Fire Identification System Based on Weighted Fusion Algorithm. Forests. 2022; 13(8):1301. https://doi.org/10.3390/f13081301

Chicago/Turabian Style

Qian, Jingjing, and Haifeng Lin. 2022. "A Forest Fire Identification System Based on Weighted Fusion Algorithm" Forests 13, no. 8: 1301. https://doi.org/10.3390/f13081301

APA Style

Qian, J., & Lin, H. (2022). A Forest Fire Identification System Based on Weighted Fusion Algorithm. Forests, 13(8), 1301. https://doi.org/10.3390/f13081301

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