Forest Fire Detection using Convolutional Neural Network Model

Sah, Shubham; Prakash, Suneet; Meena, Shweta

doi:10.1109/i2ct57861.2023.10126370

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) 2023

DOI: 10.1109/i2ct57861.2023.10126370

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Forest Fire Detection using Convolutional Neural Network Model

Shubham Sah

Suneet Prakash

Shweta Meena

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2024

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FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

Yun,

Zheng,

Lin

et al. 2024

Fire

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Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine learning forest fire detection method is limited, and it is unable to deal with complex scenes. Meanwhile, most deep learning methods are difficult to deploy due to high computational costs. To address these issues, this paper proposes a lightweight forest fire detection model based on YOLOv8 (FFYOLO). Firstly, in order to better extract the features of fire and smoke, a channel prior dilatation attention module (CPDA) is proposed. Secondly, the mixed-classification detection head (MCDH), a new detection head, is designed. Furthermore, MPDIoU is introduced to enhance the regression and classification accuracy of the model. Then, in the Neck section, a lightweight GSConv module is applied to reduce parameters while maintaining model accuracy. Finally, the knowledge distillation strategy is used during training stage to enhance the generalization ability of the model and reduce the false detection. Experimental outcomes demonstrate that, in comparison to the original model, FFYOLO realizes an mAP0.5 of 88.8% on a custom forest fire dataset, which is 3.4% better than the original model, with 25.3% lower parameters and 9.3% higher frames per second (FPS).

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FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

Yun,

Zheng,

Lin

et al. 2024

Fire

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show abstract

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Forest Fire Detection using Convolutional Neural Network Model

Cited by 1 publication

References 14 publications

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

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