Lane line detection, as the main research direction for safe driving of intelligent vehicles, can provide timely warning when the vehicle deviates from the lane, effectively alleviating traffic congestion and safety issues. However, conventional methods are easily affected by environmental factors such as light intensity and shadows, limiting their scope of use and causing significant detection errors. Therefore, a laser precise detection method for lane lines based on inverse perspective transformation is proposed. This method uses RS-LiDAR-16 LiDAR as the lane data acquisition device, uses inverse perspective transformation and top view spatial coordinate system to convert various laser point data, and uses the maximum and minimum inter class variance algorithm to find the optimal threshold of laser point reflection intensity, which serves as the basis for judging the surface and lane line data of the lane. The data of each point of the lane line is obtained through binary calculation, and these data are fitted into a line using the least squares fitting method, Finally, the lane line was detected. The experimental results show that the proposed method has high accuracy in lane line detection, and the inverse perspective transformation reduces the interference of the environment on the detection results.