With the rapid development of artificial intelligence technology, image recognition technology is playing an increasingly important role in fields such as healthcare, security, and autonomous driving. Traditional image recognition methods have limited recognition accuracy and generalization ability in complex scenarios, while the introduction of deep learning technology has greatly promoted progress in this field. This paper mainly studies image recognition technology based on deep learning, including a review of traditional methods, the evolution of new technologies, the development process of deep learning in image recognition, and the optimization directions of existing technologies. This paper also explores how deep learning can enhance the performance of image recognition, analyzes its technical advantages, and explores possible directions for improvement. The study employed the methods of literature review and comparative analysis. By collating academic papers, industry reports, and technical cases from the past, it compared the performance differences between traditional methods and deep learning methods, and analyzed the applications of typical deep learning models in image recognition. This paper finds that deep learning significantly improves the accuracy and robustness of image recognition, but still faces challenges such as strong data dependence and high computational cost. Future research can focus on lightweight models, small sample learning, and multimodal fusion.
Yiyu Pan (Wed,) studied this question.