In response to the complex imaging environment, irregular deformation of batteries, and uneven diffuse reflection of metal surfaces encountered during the automated disassembly process of retired cylindrical power lithium batteries, existing visual recognition methods are unable to accurately extract contour and pose information. We propose an accurate contour extraction method based on the Fr-chet distance similarity function and a pose detection method based on rectangles and edge morphology features. By establishing a Lambert diffuse reflection model for cylindrical lithium batteries and using morphological operation methods to obtain the rough localization contour of lithium batteries, as well as based on the similarity function defined by the Fr-chet distance, the contour is accurately extracted by classifying each pixel band in the rough localization image; Subsequently, utilizing the positive and negative terminal features of cylindrical lithium batteries, feature contours of the positive and negative terminal ROI regions are extracted employing an adaptive threshold segmentation algorithm. Finally, by comparing the rectangular values of the two end regions, the pose information of the lithium battery can be calculated. The experimental results show that in the Self-built retired cylindrical lithium battery image dataset that includes deformation, corrosion rust spots, and uneven lighting conditions, the proposed method has high accuracy in identifying lithium batteries of different models and poses. The diameter length detection error is less than 3%, and the pose detection accuracy is higher than 94%, which can meet the actual needs of automated disassembly and detection.