Object detection is the core task in computer vision, widely used in industry, medicine, agriculture, and other fields. With the development of deep learning technology, object detection algorithms have made remarkable progress in improving accuracy, improving efficiency, and reducing cost. However, the existing technology still faces challenges such as insufficient accuracy, high computational complexity, and deployment flexibility. In this paper, the basic principle of object detection is summarized in detail, including two-stage and single-stage object detection algorithms, and the advantages and disadvantages of several classical algorithms and their application scenarios are analyzed. At the same time, this paper summarizes the specific cases of the application of object detection technology in industrial production, medical diagnosis, and smart agriculture, and discusses the limitations and future development direction of the existing object detection technology. The review in this paper provides a valuable reference for further enhancing the application potential of target detection technology, and it is expected that future research can overcome the current challenges and achieve a wider application.
