WG-YOLOv5: improved YOLOv5 based on wavelet transform and GSConv for real-time wildfire detection

Luo Lu; Wang Hong

doi:10.1117/12.3031068

19 July 2024 WG-YOLOv5: improved YOLOv5 based on wavelet transform and GSConv for real-time wildfire detection

Luo Lu, Wang Hong

Author Affiliations +

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131813R (2024) https://doi.org/10.1117/12.3031068
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Wildfire detection is an important societal, economic, and environmental task. Accurately and timely identifying wildfires is crucial for protecting natural resources and ensuring the safety of human life and property. Currently, deep learning based algorithms for wildfire detection in the wild suffer from issues such as low detection accuracy, high false positive rates, and slow inference speeds. This paper proposes a wildfire detection method, WG-YOLOv5, based on the improved YOLOv5 architecture. The method introduces two-dimensional wavelet transform into the YOLOv5 network model to extract frequency domain information from wildfire images and integrates it with spatial features extracted by the backbone network. Additionally, the YOLOv5 neck structure is optimized based on GSConv. Experimental results on a collected dataset of wildfire images demonstrate that WG-YOLOv5 outperforms YOLOv5 and other comparison methods in terms of higher accuracy, lower false positive rates, and improved real-time performance. This makes it well-suited for real-time wildfire detection tasks in wild environments.

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

Citation Download Citation

Luo Lu and Wang Hong "WG-YOLOv5: improved YOLOv5 based on wavelet transform and GSConv for real-time wildfire detection", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131813R (19 July 2024); https://doi.org/10.1117/12.3031068

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KEYWORDS

Object detection

Fire

Performance modeling

Wavelet transforms

Environmental sensing

Deep learning

Feature extraction

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