Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system

A security inspection system exemplifies human-machine collaboration, and enhancing its safety and reliability through advanced technology remains a key research priority. While deep learning has incrementally improved the autonomous capabilities of security inspection equipment for automatic contra...

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Main Authors: Ying Huang, XiaoKan Wang, Yong Zhang, Li Chen, HongJi Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Artificial Intelligence
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Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1518850/full
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author Ying Huang
Ying Huang
XiaoKan Wang
XiaoKan Wang
Yong Zhang
Yong Zhang
Li Chen
Li Chen
HongJi Zhang
author_facet Ying Huang
Ying Huang
XiaoKan Wang
XiaoKan Wang
Yong Zhang
Yong Zhang
Li Chen
Li Chen
HongJi Zhang
author_sort Ying Huang
collection DOAJ
description A security inspection system exemplifies human-machine collaboration, and enhancing its safety and reliability through advanced technology remains a key research priority. While deep learning has incrementally improved the autonomous capabilities of security inspection equipment for automatic contraband detection, a gap persists between current technological capabilities and practical implementation. Recognizing that humans excel at learning, reasoning, and collaborating, while artificial intelligence offers normative, repeatable, and logical processing, we propose a human-in-the-loop hybrid augmented intelligence approach. This approach addresses the practical needs of security inspection systems by introducing a hybrid decision-making method that leverages two distinct strategies: “Reject-priority” and “Clear-priority.” These strategies play complementary roles in bolstering the decision-making process’s overall performance. Comparative experiments on a dataset from a specific security inspection site confirmed the hybrid method’s effectiveness, drawing several conclusions. This “Hybrid decision-making” method not only enhances risk perception, thereby widening the safety margin of the security inspection system, but also reduces the need for human labor, leading to increased efficiency and reduced labor costs. Additionally, it is less time-consuming, further improving the system’s overall efficiency. By integrating human and machine intelligence, this method significantly boosts decision-making effectiveness. Tailored to their unique characteristics, the method based on “Reject-priority” strategy is particularly well-suited for security inspection scenarios that demand stringent safety protocols, while the “Clear-priority” method is ideal for scenarios with high-volume traffic flow, where efficiency is paramount. As the volume of collected data grows, this approach will enable seamless adaptation of the method to evolving application needs.
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publisher Frontiers Media S.A.
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spelling doaj-art-82304a58a4094c67a5ad5c96a4f03ebd2025-01-22T07:11:10ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01810.3389/frai.2025.15188501518850Application of human-in-the-loop hybrid augmented intelligence approach in security inspection systemYing Huang0Ying Huang1XiaoKan Wang2XiaoKan Wang3Yong Zhang4Yong Zhang5Li Chen6Li Chen7HongJi Zhang8The First Research Institute of the Ministry of Public Security of P.R.C., Beijing, ChinaBeijing Zhongdun Anmin Analysis Technology Co., Ltd., Beijing, ChinaThe First Research Institute of the Ministry of Public Security of P.R.C., Beijing, ChinaBeijing Zhongdun Anmin Analysis Technology Co., Ltd., Beijing, ChinaThe First Research Institute of the Ministry of Public Security of P.R.C., Beijing, ChinaBeijing Zhongdun Anmin Analysis Technology Co., Ltd., Beijing, ChinaThe First Research Institute of the Ministry of Public Security of P.R.C., Beijing, ChinaBeijing Zhongdun Anmin Analysis Technology Co., Ltd., Beijing, ChinaBeijing Zhongdun Anmin Analysis Technology Co., Ltd., Beijing, ChinaA security inspection system exemplifies human-machine collaboration, and enhancing its safety and reliability through advanced technology remains a key research priority. While deep learning has incrementally improved the autonomous capabilities of security inspection equipment for automatic contraband detection, a gap persists between current technological capabilities and practical implementation. Recognizing that humans excel at learning, reasoning, and collaborating, while artificial intelligence offers normative, repeatable, and logical processing, we propose a human-in-the-loop hybrid augmented intelligence approach. This approach addresses the practical needs of security inspection systems by introducing a hybrid decision-making method that leverages two distinct strategies: “Reject-priority” and “Clear-priority.” These strategies play complementary roles in bolstering the decision-making process’s overall performance. Comparative experiments on a dataset from a specific security inspection site confirmed the hybrid method’s effectiveness, drawing several conclusions. This “Hybrid decision-making” method not only enhances risk perception, thereby widening the safety margin of the security inspection system, but also reduces the need for human labor, leading to increased efficiency and reduced labor costs. Additionally, it is less time-consuming, further improving the system’s overall efficiency. By integrating human and machine intelligence, this method significantly boosts decision-making effectiveness. Tailored to their unique characteristics, the method based on “Reject-priority” strategy is particularly well-suited for security inspection scenarios that demand stringent safety protocols, while the “Clear-priority” method is ideal for scenarios with high-volume traffic flow, where efficiency is paramount. As the volume of collected data grows, this approach will enable seamless adaptation of the method to evolving application needs.https://www.frontiersin.org/articles/10.3389/frai.2025.1518850/fullhuman machine collaborationhuman-in-the-loophybrid-augmented intelligencesecurity inspectioncontraband detectionworkload
spellingShingle Ying Huang
Ying Huang
XiaoKan Wang
XiaoKan Wang
Yong Zhang
Yong Zhang
Li Chen
Li Chen
HongJi Zhang
Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
Frontiers in Artificial Intelligence
human machine collaboration
human-in-the-loop
hybrid-augmented intelligence
security inspection
contraband detection
workload
title Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
title_full Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
title_fullStr Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
title_full_unstemmed Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
title_short Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system
title_sort application of human in the loop hybrid augmented intelligence approach in security inspection system
topic human machine collaboration
human-in-the-loop
hybrid-augmented intelligence
security inspection
contraband detection
workload
url https://www.frontiersin.org/articles/10.3389/frai.2025.1518850/full
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