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<p>In forest fire monitoring, unmanned aerial vehicles (UAVs) equipped with visual sensors have emerged as a pivotal tool for early detection. However, mainstream fire detection models face challenges in balancing accuracy and efficiency on resource-constrained UAV platforms, as lightweight designs often lead to reduced sensitivity to small fire targets. To address these issues, this paper introduces an efficient and lightweight forest fire detection method based on an improved YOLOv8 architecture, designed for real-time processing on UAV platforms. Our approach integrates a lightweight coordinate attention cross-stage partial network module for accurate feature extraction, a dual-convolution-based cross-scale feature fusion network for enhanced multi-scale detection, and the WIoUv3 loss function for improved localization accuracy. Experimental results on the self-constructed DIFD dataset demonstrate that the proposed method achieves an <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(mAP_{50}\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>m</mi> <mi>A</mi> <msub> <mi>P</mi> <mn>50</mn> </msub> </mrow> </math></EquationSource> </InlineEquation> of 63.9%, representing a significant improvement of 1.1% over state-of-the-art techniques. Furthermore, with a model size of only 7 MB, it achieves an optimal balance between accuracy and parameter count, making it well-suited for future deployment on edge computing devices. The code address of this paper is: <a href="https://github.com/ZJM-2000/elfired">https://github.com/ZJM-2000/elfired</a>.</p>

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Efficient lightweight fire detection in UAV imagery: an improved YOLOv8 approach

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Jie Hu,
  • Jiaming Zhang,
  • Ting Pang,
  • Bo Peng,
  • Tianrui Li

摘要

In forest fire monitoring, unmanned aerial vehicles (UAVs) equipped with visual sensors have emerged as a pivotal tool for early detection. However, mainstream fire detection models face challenges in balancing accuracy and efficiency on resource-constrained UAV platforms, as lightweight designs often lead to reduced sensitivity to small fire targets. To address these issues, this paper introduces an efficient and lightweight forest fire detection method based on an improved YOLOv8 architecture, designed for real-time processing on UAV platforms. Our approach integrates a lightweight coordinate attention cross-stage partial network module for accurate feature extraction, a dual-convolution-based cross-scale feature fusion network for enhanced multi-scale detection, and the WIoUv3 loss function for improved localization accuracy. Experimental results on the self-constructed DIFD dataset demonstrate that the proposed method achieves an \(mAP_{50}\) m A P 50 of 63.9%, representing a significant improvement of 1.1% over state-of-the-art techniques. Furthermore, with a model size of only 7 MB, it achieves an optimal balance between accuracy and parameter count, making it well-suited for future deployment on edge computing devices. The code address of this paper is: https://github.com/ZJM-2000/elfired.