Prohibited article risk detection method based on incidence relation

By constructing a knowledge map and target detection model for prohibited items, combining the item association relationship and user sub-map, the problem of insufficient multi-object association analysis in traditional methods is solved, and efficient identification and risk warning of prohibited items is achieved, which is suitable for express logistics, airport security inspection and import and export supervision scenarios.

CN120299012AActive Publication Date: 2025-07-11BEIJING UNIV OF CHEM TECH
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Patent Information

Application Number
CN202510780897.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-12
Publication Date
2025-07-11
Estimated Expiration
2045-06-12

AI Technical Summary

Technical Problem

The existing technology is difficult to identify prohibited items under strategies such as splitting, disguising or batch transportation. Traditional methods lack the ability to comprehensively judge the relationship between multiple objects and cannot effectively deal with the risks of prohibited items in complex security inspection environments.

Method used

By constructing a knowledge map of prohibited items, combining target detection models, analyzing the correlation between items, item components and their attributes, identifying potential combination and continuous prohibited items risks, introducing user sub-graph mechanisms to track item reception, and realizing logical judgments between multiple objects and cross-time risk identification.

Benefits of technology

It significantly improves the identification efficiency and risk warning capabilities of prohibited items in complex security inspection environments, and can identify hidden means such as split transportation, disguised packages and batch transfer, providing intuitive visualization of detection results and rapid response.

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Abstract

The embodiment of the invention discloses a prohibited article risk detection method based on an association relationship. The method comprises the following steps: acquiring a plurality of images of to-be-detected luggage or parcel; target detection is carried out on each image, the category and position of each entity in each image are obtained, and the entity comprises an article and an article assembly; according to the detection result of the single image, constructing a real example sub-graph of each image; according to the similarity between each real example sub-graph and a pre-constructed prohibited article knowledge graph, identifying a potential combined prohibited article risk; according to the detection results of the plurality of images, constructing a user sub-graph of each recipient; and according to the similarity between each user sub-graph and each node pair in the knowledge graph, identifying a potential continuous forbidden article risk. According to the embodiment, the combination risk of forbidden articles can be identified.
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