A visual positioning scheme for an automatic water-based loading robot was designed to solve the problem of low automation in the water-based loading process for basket-packed eggs during the processing of poultry and egg products in China. This scheme combined YOLOv5s with methods of image processing to locate and recognize basket-packed eggs in complex environments. A relationship model between the optimal segmentation threshold T and the average grayscale value M of the image was established. The dynamic threshold segmentation method was used to segment the entire stack of eggs in the image. The two types of basket-packed egg stacks were distinguished based on the aspect ratio of the minimum bounding rectangle of the stack， with the recognition accuracy of the stack type of 100%. YOLOv5s was used to locate and identify the top egg baskets of the stack， with the recognition accuracy of the model of 98.48% and the time required to detect a single image of 0.005 4 s. The image was cropped based on the results of positioning output by YOLOv5s. The rotation angles of all egg baskets were calculated by using image segmentation to segment the bounding border of the egg baskets and detecting their edge information with the Canny operators， with an average angle error of 0.41°. The pose information of all the egg baskets in the basket-packed egg stack was obtained based on the height of the egg baskets. It is indicated that the method of positioning basket-packed eggs based on YOLOv5s and image processing can accurately identify the pose information of all egg baskets in the stack. This scheme has good robustness and feasibility， and can provide visual system technology support for the automatic loading robot of basket-packed poultry eggs.