This study develops a transmission line warning device based on multi-source sensing to address the problem of real-time transmission line monitoring against mechanical collision. It also proposes a transmission line warning algorithm based on millimeter wave radar and visual fusion, and the algorithm's effectiveness is verified in a real scene. First, the front transmission lines were detected using a vision recognition algorithm based on standard deviation clustering. Second, a millimeter wave radar ranging algorithm based on an improved robust Kalman filter was used to measure the transmission line distance in real time, while visual detection was used to track the transmission line distance in real time. Finally, real-time warning and judgment for the preset mechanical collision line conditions were conducted based on the above visual detection and millimeter wave radar ranging results. The experimental results show that the visual recognition algorithm based on standard deviation clustering has an effective recognition distance of more than 20 m, a recognition accuracy of 93%, and an output frequency of 1 Hz, whereas the millimeter-wave radar ranging algorithm based on improved robust Kalman filter has a ranging accuracy of ±0.1 m, a ranging error of 2%, and an output frequency of 1 Hz. This device can meet the overall demand for real-time monitoring of transmission lines against mechanical collisions.