Evidence map›Paper›PMID 35445001›Full record

ArticleFrontiers in bioengineering and biotechnology2022

Recognition and Detection of Wide Field Bionic Compound Eye Target Based on Cloud Service Network.

Yibo Han, Xia Li, XiaoCui Li, Zhangbing Zhou, Jinshuo Li

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yibo HanNanyang Institute of Big Data Research, Nanyang Institute of Technology, Nanyang, China.
Xia LiNanyang Institute of Big Data Research, Nanyang Institute of Technology, Nanyang, China.
XiaoCui LiNanyang Institute of Big Data Research, Nanyang Institute of Technology, Nanyang, China.
Zhangbing ZhouNanyang Institute of Big Data Research, Nanyang Institute of Technology, Nanyang, China.
Jinshuo LiNanyang Institute of Big Data Research, Nanyang Institute of Technology, Nanyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, a multidisciplinary cross-fusion of bionics, robotics, computer vision, and cloud service networks was used as a research platform to study wide-field bionic compound eye target recognition and detection from multiple perspectives. The current research status of wide-field bionic compound-eye target recognition and detection was analyzed, and improvement directions were proposed. The surface microlens array arrangement was designed, and the spaced surface bionic compound eye design principle cloud service network model was established for the adopted spaced-type circumferential hierarchical microlens array arrangement. In order to realize the target localization of the compound eye system, the content of each step of the localization scheme was discussed in detail. The distribution of virtual spherical targets was designed by using the subdivision of the positive icosahedron to ensure the uniformity of the targets. The spot image was pre-processed to achieve spot segmentation. The energy symmetry-based spot center localization algorithm was explored and its localization effect was verified. A suitable spatial interpolation method was selected to establish the mapping relationship between target angle and spot coordinates. An experimental platform of wide-field bionic compound eye target recognition and detection system was acquired. A super-resolution reconstruction algorithm combining pixel rearrangement and an improved iterative inverse projection method was used for image processing. The model was trained and evaluated in terms of detection accuracy, leakage rate, time overhead, and other evaluation indexes, and the test results showed that the cloud service network-based wide-field bionic compound eye target recognition and detection performs well in terms of detection accuracy and leakage rate. Compared with the traditional algorithm, the correct rate of the algorithm was increased by 21.72%. Through the research of this paper, the wide-field bionic compound eye target recognition and detection and cloud service network were organically provide more technical support for the design of wide-field bionic compound eye target recognition and detection system.

Indexed as

cloud service networkenergy symmetryimage localizationtarget recognition detectionwide-field bionic compound eye

Identifiers

PMID35445001
PMCPMC9014010

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.