The analysis of intelligent real-time image recognition technology based on mobile edge computing and deep learning
- PDF / 3,386,040 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 1 Downloads / 182 Views
SPECIAL ISSUE PAPER
The analysis of intelligent real‑time image recognition technology based on mobile edge computing and deep learning Tao Shen1 · Chan Gao2,3 · Dawei Xu4 Received: 8 August 2020 / Accepted: 9 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This article aims to improve the accuracy of real-time image recognition in the context of the Internet of Things (IoT), reduce the core network pressure of the IoT and the proportion of IoT broadband, and meet people’s demand for internet image transmission. An intelligent image fusion system based on mobile edge computing (MEC) and deep learning is proposed, which can extract the features of images and optimize the sum of intra-class distance and inter-class distance relying on the hierarchical mode of deep learning, and realize distributed computing with the edge server and base station. Through comparison with other algorithms and strategies on the text and character data sets, the effectiveness of the constructed system is verified in the performance of the algorithm and the IoT. The results reveal that the application of the unsupervised learning hierarchical discriminant analysis (HDA) has better accuracy and recall in various databases compared with conventional image recognition algorithms. When the sum intra-class and inter-class distance K is 2, the accuracy of the algorithm can be as high as 98%. The combination of MEC and layered algorithms effectively reduces the pressures of core network and shortens the response time, greatly reduces the broadband occupancy ratio. The performance of IoT is increased by 37.03% compared with the general extraction and common cloud computing. Image recognition based on the MEC architecture can reduce the amount of network transmission and reduce the response time under the premise of ensuring the recognition rate, which can provide a theoretical basis for the research and application of user image recognition under the IoT. Keywords Internet of things · Mobile edge computing · Deep learning · Image recognition · Feature extraction · Hierarchical discriminant analysis
1 Introduction
* Tao Shen [email protected] Chan Gao [email protected] Dawei Xu [email protected] 1
College of Design and Innovation, Tongji University, Shanghai, China
2
Architecture Department, Huzhou University, Huzhou, China
3
School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
4
College of Computer Science, Qinghai Nationalities University, Xining, Qinghai, China
Economy develops rapidly these years in China, so smart city, smart network, smart shopping, and other methods have emerged, and the construction of smart network is inseparable from IoT [1]. IoT utilizes key devices such as sensors and base stations to share data through the internet, form a unified whole of all things, and achieve rapid information transmission [2]. Different types of IoT devices rely on multiple communication networks to build a complex network s
Data Loading...
