| 赖东阳,邱志斌,杨泽鼎,叶俊,罗瑞杰,彭添浩,周硕伦.基于YOLOv9-SOEP与双目立体视觉的输电线路山火火焰高度测量[J].电子测量与仪器学报,2025,39(8):258-268 |
| 基于YOLOv9-SOEP与双目立体视觉的输电线路山火火焰高度测量 |
| Flame height measurement of transmission lines wildfire based onYOLOv9-SOEP and binocular stereo vision |
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| DOI： |
| 中文关键词: 双目立体视觉 相位一致性 山火测高 目标检测 |
| 英文关键词:binocular stereo vision phase consistency wildfire height measurement target detection |
| 基金项目:国家自然科学基金(52167001)、江西省研究生创新专项资金(YC2023-S129)、江西省“双千计划”创新领军人才长期（青年）(jxsq2019101071)项目资助 |
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| Author | Institution |
| Lai Dongyang | Department of Energy and Electrical Engineering, Nanchang University, Nanchang 330031,China |
| Qiu Zhibin | Department of Energy and Electrical Engineering, Nanchang University, Nanchang 330031,China |
| Yang Zeding | Department of Energy and Electrical Engineering, Nanchang University, Nanchang 330031,China |
| Ye Jun | Department of Energy and Electrical Engineering, Nanchang University, Nanchang 330031,China |
| Luo Ruijie | School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072,China |
| Peng Tianhao | School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072,China |
| Zhou Shuolun | School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072,China |
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| 中文摘要: |
| 针对输电线路山火监测与风险预警中火焰高度测量困难的技术难题，提出一种融合YOLOv9-SOEP算法和双目立体视觉的输电线路山火火焰高度测量方法。该方法在YOLOv9网络架构的基础上引入小目标检测增强金字塔（SOEP）模块，构建了适用于输电线路山火场景的YOLOv9-SOEP改进目标检测算法；针对火焰图像纹理特征弱的问题，采用相位一致性方法实现山火双目图像的高精度特征点提取与匹配；最后通过特征点三维坐标转换与像素比例计算，建立了完整的输电线路山火火焰高度测量模型。实验结果表明，改进后的YOLOv9-SOEP目标检测模型在平均精度和召回率上分别达到了85%和89%，相比于原模型分别提升4%和19%，有效解决了小目标火焰漏检的问题；基于相位一致性的立体匹配方法能够较好地保留深度图中火焰目标的细节特征，在保证特征点充足的前提下匹配准确率达到了92%；在模拟山火火焰高度测量实验中，火焰高度的测量误差控制在648%以内，所提方法为输电线路山火监测与风险预警提供了可靠的火焰高度测量解决方案。 |
| 英文摘要: |
| To address the technical challenge of measuring flame height in wildfire monitoring and risk early warning for transmission lines, this study proposes a method for measuring wildfire flame height by integrating the YOLOv9-SOEP algorithm with binocular stereo vision. Based on the YOLOv9 network architecture, the method introduces a small object enhancement pyramid (SOEP) module to construct an improved YOLOv9-SOEP target detection algorithm tailored for transmission line wildfire scenarios. To overcome the issue of weak texture features in flame images, a phase consistency method is adopted to achieve high-precision feature point extraction and matching in binocular wildfire images. Finally, a comprehensive flame height measurement model for transmission line wildfires is established through 3D coordinate transformation of feature points and pixel ratio calculation. Experimental results demonstrate that the improved YOLOv9-SOEP model achieves an average precision and recall of 85% and 89%, respectively, representing improvements of 4% and 19% over the original model, effectively addressing the issue of missed detection for small flame targets. The phase consistency-based stereo matching method effectively preserves the detailed features of flame targets in depth maps, achieving a matching accuracy of 92% while ensuring sufficient feature points. In simulated wildfire flame height measurement experiments, the measurement error was controlled within 6.48%. The proposed method provides a reliable solution for flame height measurement in transmission line wildfire monitoring and risk early warning. |
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