Research on Image Recognition Using Deep Learning Techniques  

作　　者：Shuntao Tang Wei Chen 

机构地区：[1]Xihua University

出　　处：《控制工程期刊（中英文版）》2024年第1期1-5,共5页Scientific Journal of Control Engineering

摘　　要：This study delves into the applications,challenges,and future directions of deep learning techniques in the field of image recognition.Deep learning,particularly Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),and Generative Adversarial Networks(GANs),has become key to enhancing the precision and efficiency of image recognition.These models are capable of processing complex visual data,facilitating efficient feature extraction and image classification.However,acquiring and annotating high-quality,diverse datasets,addressing imbalances in datasets,and model training and optimization remain significant challenges in this domain.The paper proposes strategies for improving data augmentation,optimizing model architectures,and employing automated model optimization tools to address these challenges,while also emphasizing the importance of considering ethical issues in technological advancements.As technology continues to evolve,the application of deep learning in image recognition will further demonstrate its potent capability to solve complex problems,driving society towards more inclusive and diverse development.

关 键 词：Deep Learning Techniques Image Recognition Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]