Design of Real-time Object Recognition Algorithm Based on Deep Convolution Neural Network

In recent years, with the rapid development of computer vision and deep learning technology, real-time object recognition has become more and more important in various application fields. This research is devoted to designing a real-time object recognition algorithm based on Deep Convolution Neural Network (DCNN), aiming at overcoming the trade-off between accuracy and real-time performance of traditional methods. In this study, a DCNN is trained by using a large-scale labeled data set, so that it can learn rich feature representation. By introducing the hierarchical structure of deep network, the abstract ability of the model to objects is improved, and its generalization performance is enhanced by data enhancement technology. Through careful design of network structure and parameter adjustment, the model performs well in real-time object recognition tasks. In order to optimize real-time processing, we adopt a series of acceleration and optimization strategies to ensure that the algorithm can run quickly and reliably in various environments. Experiments show that the proposed algorithm achieves superior performance on several public data sets. Compared with traditional methods, the accuracy is improved by more than 8.26%, while maintaining high efficiency in real-time. The real-time object recognition algorithm based on DCNN proposed in this study has important potential value in practical application, which provides new technical support for promoting the development and application of intelligent systems.