Research on fire detection based on deformable multi-scale attention

Long Jin; Chengyu Wang; Zeke Wang; Ruihua Fang

doi:10.1117/12.3038476

23 August 2024 Research on fire detection based on deformable multi-scale attention

Long Jin, Chengyu Wang, Zeke Wang, Ruihua Fang

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132502X (2024) https://doi.org/10.1117/12.3038476
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Rapid and effective identification of flame smoke at the early stage of a fire in a home kitchen scenario is crucial for reducing casualties and property losses. In this paper, a deformable multi-scale attention-based detection model is proposed for the detection of flame and smoke targets, which has the problems of unfixed morphology and variable features. The model adopts deformable convolutional kernel and deformable attention to enhance the feature extraction ability of the backbone network for the target, and utilizes progressive pyramid network based on adaptive spatial feature fusion and efficient multiscale attention mechanism to enhance the multiscale feature fusion ability of the model. The final experimental results show that the detection accuracy of our model improves by 3.7% when compared with the best benchmark accuracy based on Yolov8s. It proves that our method can effectively detect targets such as flame smoke and prevent fires more reliably.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Long Jin, Chengyu Wang, Zeke Wang, and Ruihua Fang "Research on fire detection based on deformable multi-scale attention", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132502X (23 August 2024); https://doi.org/10.1117/12.3038476

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KEYWORDS

Deformation

Convolution

Fire

Flame

Target detection

Data modeling

Feature extraction

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