Analysis of pig posture is significant for improving the welfare and yield of captive pigs under different conditions. Detection of pig postures, such as standing, lateral lying, sternal lying, and sitting, can facilitate a comprehensive assessment of the psychological and physiological conditions of pigs, prediction of their abnormal or detrimental behavior, and evaluation of the farming conditions to improve pig welfare and yield. With the introduction of smart farming into the farming industry, effective and applicable posture detection methods become indispensable for realizing the above purposes in an intelligent and automatic manner. From early manual modeling to traditional machine vision, and then to deep learning, multifarious detection methods have been proposed to meet the practical demand. Posture detection methods based on deep learning show great superiority in terms of performance (such as accuracy, speed, and robustness) and feasibility (such as simplicity and universality) compared with most traditional methods. It is promising to popularize deep learning technology in actual commercial production on a large scale to automate pig posture monitoring. This review comprehensively introduces the data acquisition methods and sub-tasks for pig posture detection and their technological evolutionary processes, and also summarizes the application of mainstream deep learning models in pig posture detection. Finally, the limitations of current methods and the future directions for research will be discussed.