The analysis of intelligent real-time image recognition technology based on mobile edge computing and deep learning

Shen, Tao; Gao, Chan; Xu, Dawei

doi:10.1007/s11554-020-01039-x

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…By optimizing the resource allocation and service layout, the total computing time and energy consumption of all users can be minimized. Aiming at the delay problem in real-time image processing, Shen et al [20] designed an intelligent recognition technology based on deep learning to process real-time image. To improve the efficiency of scheduling decision for the application with dependent tasks, Sowndarya et al [21] proposed an Individual Time Allocation method with Greedy Scheduling (ITAGS) to minimize the application execution cost.…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption and Time-Delay Optimization of Dependency-Aware Tasks Offloading for Industry 5.0 Applications

Xu,

Lv,

Zhang

et al. 2024

IEEE Trans. Consumer Electron.

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With the development of network and communication technology, artificial intelligence, distributed computing and beyond fifth-generation communications, Industry 5.0 is booming and obtains rapid growth. To improve the processing efficiency of intensive tasks, Mobile Edge Computing (MEC) technology can facilitate task offloading from mobile devices to edge servers. Traditional methods do not fully consider that applications are usually composed of dependency-aware tasks, which neglect the impact of task dependencies on offloading strategies and lead to low efficiency in task scheduling. This paper proposes a joint optimization of energy consumption and time delay for dependency-aware task offloading with mobile edge computing. First, in order to minimize the energy consumption and task processing of mobile device, a dependency-aware task offloading model is established. Secondly, the dependencies between tasks are analyzed to construct a Directed Acyclic Graph (DAG), and an algorithm based on topological ordering is introduced to obtain possible solutions for task scheduling. Furthermore, to minimize the total cost, an improved Particle Swarm Optimization (PSO) algorithm is used to obtain the optimal task offloading decision and MEC server selection optimization. Experimental results demonstrate that the proposed strategy can reduce the time cost and energy consumption compared to existing typical methods for tasks with different dependencies effectively.

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Section: Related Workmentioning

confidence: 99%

Energy Consumption and Time-Delay Optimization of Dependency-Aware Tasks Offloading for Industry 5.0 Applications

Xu,

Lv,

Zhang

et al. 2024

IEEE Trans. Consumer Electron.

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show abstract

“…It originated from the Microsoft COCO dataset which was funded and annotated by Microsoft in 2014. The image includes 91 types of targets, 328,000 images, and 2,500,000 labels, and the number of individuals in the entire data set exceeds 1.5 million [17].…”

Section: Image Sensormentioning

confidence: 99%

Preschool Education Interactive System Based on Smart Sensor Image Recognition

Chen¹,

Jin²

2022

Wireless Communications and Mobile Computing

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The purpose of this research is to use the image recognition technology of smart sensors to establish a preschool education system for preschool children, which can realize interactive image-based learning and education for preschool children. This paper uses CMOS image sensor (IV2-6500CA of KEYENCE) to design the front-end image acquisition hardware system and establishes an image recognition system that can realize most of the recognition functions based on BP neural network algorithm and ImageNet, MS COCO, MNIST, and the Chars74K dataset and integrate the image recognition system into the preschool education interactive system. The image recognition system has a high accuracy rate, with an overall accuracy rate of 85.16%. Compared with the traditional preschool education system, it has a higher recognition rate, better teaching efficiency, and interactivity. It can recognize most of the objects that children touch and have a good interactive education effect.

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“…This also requires technicians to pre-process the image before specific recognition of the image. The preprocessing of the image is the basis of the entire image recognition process [5], and even directly determines the reliability of image recognition.…”

Section: The Recognition Process Of Computer Intelligent Imagesmentioning

confidence: 99%

Research on algorithms of computer image intelligent recognition and deep learning

Shao

Zhou²

2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

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In recent years, with the rapid development of science and technology in my country, people's requirements for computer information technology have become higher and higher. In order to better explore new things, engineers and technicians need to continuously optimize computer information processing systems to help people better solve practical problems. The article mainly analyzes the development process of computer intelligent image recognition algorithms, expounds the computer intelligent image recognition algorithm, and finally puts forward the technical application of computer intelligent image recognition algorithm, which provides a certain research foundation for related staff.

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The analysis of intelligent real-time image recognition technology based on mobile edge computing and deep learning

Cited by 16 publications

References 22 publications

Energy Consumption and Time-Delay Optimization of Dependency-Aware Tasks Offloading for Industry 5.0 Applications

Energy Consumption and Time-Delay Optimization of Dependency-Aware Tasks Offloading for Industry 5.0 Applications

Preschool Education Interactive System Based on Smart Sensor Image Recognition

Research on algorithms of computer image intelligent recognition and deep learning

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