This paper proposes a external parameter calibration method for lidar and cameras based on line features. First, the image is coarsely segmented using the proportional-integral-derivative network, and the image line features are obtained through fine segmentation via image post-processing operation. Second, a clustering operation is performed on the point cloud data, and the clustered objects are filtered based on intensity, morphology, and other information to retain the line features in the lidar point cloud. Third, a matching consistency function is constructed to determine the degree of matching between the image and lidar line features. Finally, the external parameter between the lidar and the camera is obtained by maximizing the matching consistency function. Experiments on dataset collected by a real vehicle demonstrate that the proposed method has lower calibration errors compared to the benchmark method. Specifically, the proposed method reduces the average calibration error by 0.179° in rotation parameter and by 0.2 cm in translation parameter, meeting the average calibration accuracy requirements for real-world applications.