Object Detection and Recognition Based on Deep Learning

With the rapid development of artificial intelligence technology, deep learning has shown strong potential in the field of object detection and recognition. In this paper, the basic principle of deep learning and its application in target detection and recognition are introduced. Then, this paper describes the characteristics and advantages of the selected deep learning algorithm and the construction process of the experimental environment. In the aspect of data set preparation, this paper adopts the widely recognized standard data set and preprocesses it to improve the training efficiency and recognition accuracy of the model. In the part of model training and optimization, the performance of the model is optimized by adjusting the network structure, learning rate, batch size and other parameters, and the effectiveness of the proposed method is verified by comparative experiments. The experimental results show that the object detection and recognition model based on deep learning has achieved good recognition results in all kinds of complex scenes. The advantages and limitations of the model are analyzed, which provides a valuable reference for the subsequent research.