This paper introduced the development of object detection datasets and the establishment of basic evaluation metrics, and based on this, it reviewed different categories of object detection algorithms. Single-stage and two-stage detection algorithms, as well as corresponding optimization algorithms, were analyzed separately. Highlighting the iterative process of detection speed and accuracy, the paper elaborated the challenges and difficulties in object detection algorithms. A summary and outlook for the improvement of the method itself and the optimization design under the application requirements of the algorithm were proposed in the paper, which indicated training supervision of object detection, the difficulty of detecting small targets by the algorithm. At the same time, the paper also indicated the coordination between detection speed and accuracy in real-time detection tasks and multimodal fusion application, as well as the important significance of the interpretability of algorithm operation for further improving the algorithm.