Design pattern detection is a very important research topic in the field of software engineering. Many scholars both domestically and internationally have dedicated their efforts to researching and resolving the design pattern detection issue, achieving fruitful results. This paper reviews the current technologies in software design pattern detection and looks ahead to their future prospects. Firstly, this paper briefly introduces the development history of software design pattern detection, discusses the object of design pattern detection, summarizes the feature types of design patterns, and gives the evaluation indexes of design pattern detection. Then, it summarizes the existing classification methods for design pattern detection techniques, and introduces the classification method of this paper. Next, according to the development timeline of design pattern detection technologies, the current research status and the latest advancements of software design pattern detection technologies are discussed from three broad approaches: non-machine learning design pattern detection, machine learning design pattern detection, design pattern detection based on pre-trained language models, and the current achievements are compared and summarized. Finally, the main problems and challenges in this field are analyzed, and further research directions and potential solutions are pointed out. Covering from early non-machine learning methods through the utilization of machine learning technologies to the application of modern pre-trained language models, this paper comprehensively and systematically presents the development history, the latest advancements, and the future prospects of the field. It provides valuable guidance for future research directions and thinking within this area.