Image Real-Time Detection Using LSE-Yolo Neural Network in Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes

Author:

Zhi-Xian Zheng1,Zhang Fuquan23ORCID

Affiliation:

1. Fujian Chuanzheng Communications College, Fuzhou Fujian, 350007, China

2. College of Computer and Control Engineering, Minjiang University, Fuzhou, 350108, China

3. Digital Media Art, Key Laboratory of Sichuan Province, Sichuan Conservatory of Music, Chengdu, 610021, China

Abstract

In this paper, a novel visual image real-time detection LSE-Yolo neural network is presented, which is in artificial intelligence-based Internet of Things for smart cites and smart homes. Despite the great achievements that have been acquired in image detection, the issue of visual image real-time detection combined with privacy data protection to serve for smart cities and smart homes has been overlooked. The technique we applied in our study is referred to as visual object detection, which can contribute to more healthy and comfortable life. When several studies have been carried out to test the validity, it is suggested that our proposed LSE-Yolo neural network has better performance in image real-time detection based on AIoT for smart cities and smart homes. And it is similar to state-of-the-art. The fruitful work has made great contributions to our present understanding of the visual image detection serving for smart cities and smart homes.

Funder

Minjiang College 2021 School-Level Scientific Research Project Funding

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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