During power transmission line construction, the intrusion of large-scale engineering vehicles into the safety clearance area poses serious threats to operational and construction safety. Existing monitoring approaches mainly rely on manual inspections supplemented by single-sensor devices such as cameras or infrared sensors. Although these methods improve automation to some extent, they remain prone to missed detections and false alarms under complex environmental conditions. To address these challenges, this paper proposes an intelligent monitoring and early-warning system for clearance safety in power transmission line construction. The system combines data from a visible-light camera and a LiDAR sensor to enhance spatial perception and clearance assessment. A manual calibration method is used to align the coordinate relationship between the camera and LiDAR, improving spatial consistency between images and point clouds. The YOLOv11 algorithm is employed to detect engineering vehicles, and the detection results are combined with the corrected point cloud data to obtain the 3D location of the target’s highest point and estimate the safety clearance distance. Experimental results demonstrate that the system operates stably in outdoor environments, achieving a projection error of less than 5 cm and a YOLO-based detection accuracy of 93.1%. The proposed system effectively provides early warnings for vehicles entering the protection zone of high-voltage transmission lines, featuring low power consumption, high accuracy, and easy deployment, thereby supporting intelligent safety management in field construction scenarios.