With the large-scale application of deep learning in the field of object detection, the accuracy and speed of object detection technology have been rapidly improved, and it has been widely used in many fields, including pedestrian detection, face detection, text detection, traffic sign and signal light detection, and remote sensing image detection. This study reviews object detection technology based on the investigation of relevant domestic and foreign literature. First, the research status of object detection as well as the datasets and performance indicators for object detection algorithm tests are introduced. In this paper, two kinds of typical object detection algorithms with different architectures, namely two-stage object detection algorithms based on region proposals and one-stage object detection algorithms based on regression analysis, are described elaborately in their process architectures, performance effect, advantages, and disadvantages. In addition, some new object detection algorithms developed in recent years have been supplemented, and the experimental results and advantages and disadvantages of various algorithms on mainstream datasets are listed. Finally, some common application scenarios of object detection are specified, and future development trends are analyzed considering current research hotspots.