Design pattern detection is an essential research topic in software engineering. Many scholars both domestically and internationally have dedicated their efforts to researching and resolving design pattern detection, thereby yielding fruitful results. This study reviews the current technologies in software design pattern detection and points out their prospects. Firstly, this study briefly introduces the development history of software design pattern detection, discusses the objects of design pattern detection, summarizes the feature types of design patterns, and provides the evaluation indexes of design pattern detection. Then, the existing classification methods for design pattern detection techniques are summarized, and the classification method proposed in this study is introduced. Next, according to the development timeline of design pattern detection technologies, the research status and latest advancements of current software design pattern detection technologies are discussed from three approaches, including non-machine learning design pattern detection, machine learning design pattern detection, and design pattern detection based on pre-trained language models, with the current achievements summarized and compared. Finally, the main problems and challenges in this field are analyzed, and further research directions and potential solutions are pointed out. Covering contents from early non-machine learning methods and utilization of machine learning technologies to the application of modern pre-trained language models, this study comprehensively and systematically presents the development history, latest advancements, and prospects of this field. It provides valuable guidance for future research directions and ideas within this area.