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Research on Forest Fire Detection Algorithm Based on Improved YOLOv5
Version 1
: Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (07:20:56 CEST)
A peer-reviewed article of this Preprint also exists.
Li, J.; Lian, X. Research on Forest Fire Detection Algorithm Based on Improved YOLOv5. Mach. Learn. Knowl. Extr. 2023, 5, 725-745. Li, J.; Lian, X. Research on Forest Fire Detection Algorithm Based on Improved YOLOv5. Mach. Learn. Knowl. Extr. 2023, 5, 725-745.
Abstract
Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding attention mechanism in the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection effect of the model and the robustness of the model. In addition, random Mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model responsibility to improve the detection speed. Experimental results on homemade forest fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.
Keywords
forest fire detection; attention mechanism; staged object detection; deep learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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