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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| 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. |
| format | Article |
| id | doaj-art-82304a58a4094c67a5ad5c96a4f03ebd |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| 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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