Investigation of the theory and applications of deep learning-based image recognition

Abstract

Image recognition technology, as an important branch in the field of artificial intelligence, aims to process and analyze images to recognize objects in images. The aim of this paper is to summarize the general approach to implementing image recognition and the application of this technology in some fields by analyzing relevant articles in the field of image recognition in recent years and to suggest possible conjectures on the future direction of this technology. This paper points out that the image recognition process is divided into two main parts: image processing and image recognition. For the implementation of the corresponding algorithms, this paper introduces two commonly used software libraries: OpenCV and YOLOv5. In addition, the paper also focuses on the applications and contributions of image recognition techniques to face recognition, lane detection, and medical image analysis. It is also pointed out that image recognition technology has a very promising future, and that there is still a lot of further improvement in the field of medical image analysis.

Optimizing Image Recognition Algorithms with Differential Privacy Integration

“…With the rapid development of artificial intelligence technology, image recognition has become one of the core tasks in the field of computer vision [1,2]. Image recognition technology, widely applied in various industries, significantly improves work efficiency and decision accuracy through the automatic analysis and processing of image data [3][4][5]. However, image data often contains a large amount of sensitive information.…”