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.