The periodic monitoring of energy lines to lessen the impacts of threats and to destroy the potential risks against the power transmission lines (PTL) is highly important. The risks can involve natural causes (vegetation, landslides, trees, avalanches, storms, etc.) on the one hand and the human factor (constructions and buildings breaking the safety distance, dumping the excavated material, theft, etc.) on the other hand. In this study, an algorithm, which can automatically detect PTLs' wires and pylons using the UAV LiDAR data, is developed. Specific safety distances are also spatially analyzed and the existence of risky ground objects is examined with the help of the detected PTLs. In the newly developed algorithm, ground points are located and the low object points in vertical distance to these ground points are eliminated using the cloth simulation filtering (CSF) method. The remaining point cloud is separated into voxels of 5x5x5m in size. In the search of 26 neighbor voxels, starting from automatically determined seed voxel, final detection of wire and pylons has been determined by the algorithm of "concave hull" after their straight slopes which are fitted by height values variant and RANSAC were analyzed. The accuracy value for wires was 97.13%, the integrity value was 97.36%, the quality value was 94.63%, for the pylons the accuracy value was 70.25%, the integrity value was 94.24% and the quality value was 67.36% in the algorithm developed. Periodic applications based on the proposed approach will make it easy to monitor and maintain PTLs components (wires and pylons) without time-consuming field works.