<p>Pipe alignment is a necessary step for automatic pipe assembly. Image-based visual servoing (IBVS) offers an effective solution for achieving automatic pipe alignment. Due to the complex pose of the pipe, the conventional teach-by-show (TBS) mode in IBVS fails to obtain the accurate desired image. Additionally, dynamic noise and the texture-less surface of the pipe make feature extraction difficult. In this work, we replace the entire pipe with a local two-segment pipe and introduce an IBVS system for pipe assembly. We use a virtual camera to calculate the desired image to avoid manual teaching. To simplify the desired image by excluding arc information, we design a feature composed of four lines and a point. For the pipe geometry, only the pipe diameter and bending angle are required as prior knowledge, while the virtual-camera distances are preset to define the desired image. Then, a feature extraction method is developed to provide stable feedback through semantic segmentation, line fitting and classification, and point feature enhancement. The alignment experiments conducted on pipes of different shapes demonstrate the effectiveness of the proposed method.</p>