The distance from power lines to ground is an important indicator for transmission line inspection and power grid risk assessment. However, traditional manual measurement methods are difficult to operate and inefficient due to complex environmental and terrain conditions. Based on UAV LiDAR point cloud data, an efficient intelligent detection technology process is proposed, which includes point cloud classification, bottom-layer transmission line extraction, distance detection to the ground, and risk point identification. The main innovations are: (1) By projecting the three-dimensional point cloud of the transmission line into the two-dimensional residual space, the point cloud segmentation process is transformed into a projection point clustering problem, which dilutes the negative impact of data missing and point cloud noise factors on the fine segmentation of a single transmission line; (2) An identification and extraction strategy for the lowest transmission line is designed based on cluster barycentric analysis; (3) The intelligent detection of transmission line to ground distance is realized based on laser point cloud. The experimental results show that this method can quickly and accurately identify the lowest transmission line from the original point cloud data, and realize the efficient detection of ground distance and accurate identification of risk points. It has the characteristics of simple operation and high automation. This research provides a theoretical and practical reference for safety inspection of airborne LiDAR transmission lines.