To address the challenges of low detection accuracy and high false positive rates of transmission lines in UAV (Unmanned Aerial Vehicle) images, we explore the linear features and spatial distribution. We introduce an enhanced stochastic Hough transform technique tailored for detecting transmission lines in complex backgrounds. By employing the Hessian matrix for initial preprocessing of transmission lines, and utilizing boundary search and pixel row segmentation, our approach distinguishes transmission line areas from the background. We significantly reduce both false positives and missed detections, thereby improving the accuracy of transmission line identification. Experiments demonstrate that our method not only processes images more rapidly, but also yields superior detection results compared to conventional and random Hough transform methods.

翻译：针对无人机图像中输电线路检测精度低、误检率高的问题，本文探究了其线性特征与空间分布特性。我们提出了一种增强型随机霍夫变换技术，专用于复杂背景下的输电线路检测。通过采用海森矩阵对输电线路进行初始预处理，并利用边界搜索与像素行分割方法，将输电线路区域与背景区分开来。该方法显著降低了误检与漏检率，从而提升了输电线路识别的准确度。实验表明，与传统及随机霍夫变换方法相比，我们的方法不仅图像处理速度更快，而且检测结果更优。