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

Ying Huang ^1,2 Xiaokan Wang ^1,2 Yong Zhang ^1,2 Li Chen ^1,2 Hongji Zhang ^2*

• ¹ First Research Institute of the Ministry of Public Security of People’s Republic of China, Beijing, China
• ² ZhongDun AnMin, Beijing, China

A proposed human-in-the-loop hybrid augmented intelligence approach addresses the practical needs of security inspection systems by introducing a hybrid decisionmaking method that leverages two distinct strategies: "Reject-priority" and "Clearpriority." 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.