Affiliations: Academy of Art & Design, Nanchang Institute of Technology, Nanchang, Jiangxi, China | E-mail: [email protected]
Correspondence:
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Corresponding author: Academy of Art & Design, Nanchang Institute of Technology, Nanchang, Jiangxi, China. E-mail: [email protected].
Abstract: In order to enhance the performance of intelligent image recognition, this study optimizes the image recognition model through lightweight convolutional neural networks (CNNs) and cloud computing technology. The study begins by introducing the relevant theories and models of edge computing (EC) and lightweight CNNs models. Next, this study focuses on optimizing traditional image recognition models. Finally, the effectiveness and reliability of the proposed model are experimentally validated. The experimental results indicate that, when recognizing 1000 images, the average recognition times per image on cloud servers and edge servers are 13.33 ms and 50.11 ms, respectively. Despite the faster speed of cloud servers, the performance of edge servers can be improved by stacking servers. When the number of edge servers reaches 4, their recognition speed surpasses that of the cloud server model. Additionally, comparing the latency and processing time between EC and cloud computing architectures, it is observed that, with an increase in the number of processed images, the average processing time per image in the EC architecture remains relatively stable and consistent. In contrast, the average processing time gradually increases in the cloud computing architecture. This indicates a significant impact of the number of images on the processing rate of the cloud computing architecture. Therefore, as the time gap in processing between cloud computing and EC increases, the advantages of the EC architecture become more apparent. This study’s significance lies in advancing the development of deep learning technology and providing possibilities for its widespread practical application. The contribution of this study lies in promoting the development of EC and lightweight neural network models, offering valuable references and guidance for practical applications in related fields.
