Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System

People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device syste...

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Veröffentlicht in:Security and communication networks Jg. 2022; S. 1 - 10
Hauptverfasser: Li, Xiang, He, Donggang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Hindawi 19.05.2022
John Wiley & Sons, Inc
Schlagworte:
Algorithms
Animals
Computer networks
Computers
Edge computing
Electronic devices
Embedded systems
Endurance
Image acquisition
Image processing
Medical equipment
Military technology
Object recognition
Semantics
Sensors
Surveillance
Target detection
Target recognition
ISSN:1939-0114, 1939-0122
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Abstract People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.
AbstractList People may quickly employ imagig devices to acquire and use image data thanks to the rapid development of computer networks and communication technologies. However, imaging devices obtain massive data through real-time acquisition, and a large number of invalid images affect the imaging device system’s endurance on the one hand while also requiring a significant amount of time for analysis on the other hand, so there is a critical need to find a way to automate the mining of valuable information in the data. In this paper, we propose an intelligent imaging device system, which embeds a target intelligent recognition algorithm, improves the YOLOv3 model by using a method based on depth-separable convolutional blocks and inverse feature fusion structure, and finally achieves fast target detection while improving detection accuracy through the design of distance-based nonextreme suppression and loss function. By preprocessing the images and automatically identifying and saving images containing target animals, the range of the imaging device system equipment can be improved and the workload of researchers searching for target animals in images can be reduced. In this paper, we propose a method for intelligent preservation of contained target images by deploying lightweight target recognition algorithms on edge computing hardware with the intelligence of the intelligent imaging device system as the research goal. After simulation experiments, the use of this method can improve the endurance of the imaging device system and reduce the time of manual processing at a later stage.
Author Li, Xiang
He, Donggang
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ContentType Journal Article
Copyright Copyright © 2022 Xiang Li and Donggang He.
Copyright © 2022 Xiang Li and Donggang He. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
Copyright_xml – notice: Copyright © 2022 Xiang Li and Donggang He.
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SubjectTerms Algorithms
Animals
Computer networks
Computers
Edge computing
Electronic devices
Embedded systems
Endurance
Image acquisition
Image processing
Medical equipment
Military technology
Object recognition
Semantics
Sensors
Surveillance
Target detection
Target recognition
Title Image Processing and Recognition Algorithm Design in Intelligent Imaging Device System
URI https://dx.doi.org/10.1155/2022/9669903
https://www.proquest.com/docview/2671101414
Volume 2022
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