Target detection plays an important role in the field of computer vision and artificial intelligence, and has been widely used in the fields of object recognition and localization, real-time monitoring and tracking, scene understanding, and intelligent analysis. This paper takes the development of target detection algorithms as the main line, firstly introduces the traditional target detection algorithms, analyzes their advantages and summarizes them for their shortcomings, then analyzes the algorithmic flow and advantages and disadvantages of the two-stage target detection algorithm based on deep learning and the single-stage target detection algorithm in detail, and carries out a comparative analysis on the public dataset to validate their performances and practicability. The improvement ideas of the latest target detection algorithms are also presented, which show better improvement in both speed and accuracy. Finally, this paper provides an outlook on the future development trend in the field of target detection, pointing out the research directions and ideas that can be drawn upon.