Condition-Specific AI Sorting Support for X-Ray Article Detection
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Solution Overview
Problem
The increasing workload of inspection staff in customs and logistics due to the clever concealment of prohibited and restricted articles requires more efficient sorting support systems.
Innovation Solution
A sorting support apparatus and method utilizing AI-based learning models optimized for specific conditions, such as location, time, and sender, to determine the presence of prohibited articles in inspection targets, reducing the need for manual labor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If inspections are carried out by staff using X-ray scanning and manual methods, then detection capability is maintained, but staff workload increases due to clever concealment of prohibited articles
Solution Approach 1:
The patent replaces manual inspection methods with an automated AI-based image recognition system. The determination part automatically analyzes transmission images to detect prohibited articles, substituting the mechanical manual inspection process with an automated computational system that reduces staff workload while maintaining or improving detection capability.
Solution Approach 2:
The system enables self-service inspection through automated analysis. The sorting support apparatus independently processes transmission images and makes determination results without requiring continuous manual intervention, allowing the inspection system to serve itself and reduce dependency on human staff for routine detection tasks.
2Measurement precision
If multiple learning models are used for different usage conditions, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects appropriate learning models based on usage conditions. The determination part automatically determines which learning model to use based on the specific inspection context, allowing the system to adapt its complexity level to the task requirements rather than maintaining fixed high complexity for all scenarios.
Solution Approach 2:
The patent segments the detection task into multiple specialized learning models, each optimized for specific usage conditions or article types. This segmentation allows the system to achieve high detection accuracy for different categories while managing overall complexity through modular organization of specialized models rather than requiring one complex universal model.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly reduces staff workload by improving detection accuracy and minimizing misjudgments through targeted learning models, enhancing the efficiency of inspections.
Implementation Method 1
an input part that inputs a transmission image obtained by radiating an inspection target with electromagnetic waves
Data Source
AI summary
A sorting support apparatus is provided with: an input part that inputs a transmission image obtained by radiating an inspection target with electromagnetic waves; a storage part that stores a plurality of learning models optimized respectively for at least one article and being associated with an assumed usage condition; and a determination part that selects one of the learning models based on a specified usage condition and uses the learning model to determine whether or not the one or more articles is contained in the inspection target.