AI X-Ray Sorting Support for Condition-Specific Contraband 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 a more efficient sorting support system.
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 staff workload by automating the sorting process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection by staff is used to detect cleverly concealed prohibited articles, then detection reliability is improved, but staff workload increases and productivity decreases
Solution Approach 1:
The patent replaces manual mechanical inspection by staff with an automated X-ray scanning system that captures images and uses AI learning models to detect prohibited articles. This substitution maintains high detection reliability while eliminating staff workload in manual inspection tasks.
Solution Approach 2:
The system enables self-service inspection where the X-ray imaging and AI analysis automatically perform the detection function without requiring staff intervention. The learning models independently analyze images and identify prohibited articles, making the inspection process self-sufficient.
2Device complexity
If a single learning model is used for all inspection conditions, then device complexity is reduced, but detection precision deteriorates due to varying usage conditions
Solution Approach 1:
The patent segments the inspection task by creating multiple learning models, each optimized for specific usage conditions such as different article types, concealment methods, or inspection scenarios. This segmentation allows each model to specialize in particular detection tasks, thereby maintaining high detection precision across varying conditions.
Solution Approach 2:
The system changes the parameters of the learning models by training them with different datasets and optimization criteria suited to specific usage conditions. Each model has tailored parameters that maximize its effectiveness for particular inspection scenarios, improving overall detection precision.
3Measurement precision
If multiple learning models are used for different usage conditions, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a dynamic model selection mechanism that automatically chooses the appropriate learning model based on the current usage condition or inspection scenario. This dynamic adaptation allows the system to maintain high detection precision while managing complexity through automated selection rather than manual configuration.
Solution Approach 2:
The system uses feedback from the inspection context and usage conditions to select the most suitable learning model. By continuously monitoring the inspection scenario and adjusting model selection accordingly, the system maintains high precision without requiring complex manual intervention.
4Ease of operation
If conventional inspection methods are used, then ease of operation is maintained, but loss of time increases due to increasing staff workload
Solution Approach 1:
The patent replaces time-consuming manual inspection with automated X-ray imaging and AI analysis. This substitution maintains ease of operation through simple system operation while dramatically reducing inspection time by processing images and detecting prohibited articles much faster than manual methods.
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 tailored learning models, enhancing the efficiency of inspections at customs and logistics sites.
Implementation Method 1
X-ray scanning apparatuses are used in these inspections
Implementation Method 2
inspections by electromagnetic radiation in a range of 200 MHz-1 THz
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.