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AOI-OPEN: federated operation and control for DAO-based trustworthy and intelligent AOI ecology
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Yansong CAO ’1, Yutong WANG ’2 , Jing YANG ’2 , Yonglin TIAN ‡ 2 , Jiangong WANG2, Fei-Yue WANG1,2. AOI-OPEN: federated operation and control for DAO-based trustworthy and intelligent AOI ecology[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="AOI-OPEN: federated operation and control for DAO-based trustworthy and intelligent AOI ecology",
author="Yansong CAO ’1, Yutong WANG ’2 , Jing YANG ’2 , Yonglin TIAN ‡ 2 , Jiangong WANG2, Fei-Yue WANG1,2",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400975"
}
%0 Journal Article
%T AOI-OPEN: federated operation and control for DAO-based trustworthy and intelligent AOI ecology
%A Yansong CAO ’1
%A Yutong WANG ’2
%A Jing YANG ’2
%A Yonglin TIAN ‡ 2
%A Jiangong WANG2
%A Fei-Yue WANG1
%A 2
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400975
TY - JOUR
T1 - AOI-OPEN: federated operation and control for DAO-based trustworthy and intelligent AOI ecology
A1 - Yansong CAO ’1
A1 - Yutong WANG ’2
A1 - Jing YANG ’2
A1 - Yonglin TIAN ‡ 2
A1 - Jiangong WANG2
A1 - Fei-Yue WANG1
A1 - 2
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400975
Abstract: Isolated data islands are prevalent in intelligent automated optical inspection (AOI) systems, limiting the full utilization of data resources and impeding the potential of AOI systems. Establishing a collaborative ecology involving software providers, hardware manufacturers, and factories offers an encouraging solution to build a closed-loop data flow and achieve optimal data resource utilization. However, concerns about privacy issues, rights infringement, and threats from other participants present challenges in establishing an efficient and effective community. In this paper, we propose a novel framework, AOI‐ OPEN, that first creates a trustworthy AOI ecology to gather related entities with decentralized autonomous organization (DAO) mechanisms. Then, a parallel data pipeline is proposed to generate large-scale virtual samples from small-scale real data for AOI systems. Finally, Federated Learning (FL) is adopted to utilize the distributed data resource among multiple entities and build large privacy-preserving models. Experiments on defect classification tasks show that, with privacy preserved, AOI‐ OPEN greatly strengthens the utilization of distributed data resources and improves the accuracy of inspection models.
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