Object detection is a research hotspot in the field of computer vision. In recent years, the deep learning algorithms contributing to object detection has developed by leaps and bounds. Objection detection algorithms based on deep learning can be roughly divided into two categories depending on candidate regions and regression, respectively. The object detection algorithms based on candidate regions have high accuracy, but complex structure and low speed of detection. The object detection algorithms based on regression, contrarily, have simple structure, high speed of detection, and thus more applications in the field of real-time object detection, but its detection is with low accuracy. This paper summarizes the mainstream algorithms of object detection based on deep learning and analyzes the advantages and disadvantages of different algorithms and their applications. Finally, this paper predicts the prospects of deep learning-based object detection algorithms according to the existing challenges.