The contact tip to workpiece distance (CTWD) has a considerable effect on the current of the wire arc additive manufacturing (WAAM) process, which in turn affects the height of the deposited layer. An unstable CTWD may result in significant deviation in the height direction. Accordingly, it is necessary to monitor and regulate the CTWD for each individual layer. In practice, CTWD can be calculated from the gaps between the torch and the current layer edge. An image-based layer height estimation method is developed to ascertain the height of the newly deposited layer. A welding camera, affixed to the torch, is utilized to document the deposition process. A segmentation network is utilized to identify the perimeter of the newly deposited layer. Given that the camera is continuously focused on the molten pool, it is essential to ascertain the camera's position to calculate the layer height. This is achieved by synchronizing the robot position data and the images in ROS2 (Robot operation system 2) to locate the position of camera in the real world. To find out a better choice, three distinct deep learning-based segmentation algorithms, namely Unet3 +, YOLOv11, and PIDNet, are evaluated in terms of accuracy and efficiency. Then, the layer height estimation method is tested with a 10-layer thin wall. As a result, the proposed method can provide accurate height estimation. Among the segmentation algorithms, PIDNet using 256x256 resolution is considered as a better choice to balance the accuracy and efficiency.