Research on Deep Learning Architecture Design System in Optoelectronic Information Processing

This paper proposes a deep learning architecture design system for optoelectronic information processing, aiming to improve the efficiency and accuracy of optoelectronic information processing. With the increasing demand for efficient information processing in fields such as optical imaging and optical communication, traditional methods face many challenges in processing complex optoelectronic information, such as high computational complexity and sensitivity to noise. Deep learning technology provides a new solution for optoelectronic information processing with its powerful feature extraction and pattern recognition capabilities. The core of this paper is the original optoelectronic information optimization network (OIONet), which can automatically focus on the key features in optoelectronic information while suppressing noise interference by introducing an attention mechanism based on multi-scale feature fusion. This paper verifies the OIONet model on multiple standard datasets. It is found that the average processing precision of OIONet is 20.5% higher than that of traditional methods and existing deep learning models, and the reduction of reasoning time is 45.2%. Furthermore, the performance of the model is demonstrated by visual analysis, which further proves its validity.