Cargo Image Inspection Through Third-Party AI Integration
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Solution Overview
Problem
Current security inspection systems at transportation centers struggle to accurately detect contraband in cargo containers due to limitations in integrating advanced artificial intelligence models and face challenges in sharing these models across different jurisdictions, leading to inefficiencies and potential human errors in threat assessment.
Innovation Solution
A system and method that enables the integration of multiple AI models, each tailored to specific cargo types or threats, by using a common hosted service to apply these models to inspection image data, allowing for efficient correlation with manifest data and displaying results to operators, while supporting diverse native programming formats and facilitating model sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection is used to analyze images for threat detection, then human operators can interpret images, but the process is prone to human errors and adds excessive time
Solution Approach 1:
The patent replaces manual inspection with automated image analysis systems including machine learning models and artificial intelligence algorithms. These systems automatically process cargo images, compare them against manifest data, and identify potential threats without human intervention, thereby eliminating human error and reducing inspection time while maintaining or improving detection accuracy.
Solution Approach 2:
The system enables self-service inspection by implementing automated comparison between images and manifest data. The inspection system independently performs threat detection, cargo verification, and anomaly identification without requiring manual analysis, allowing the system to serve its own inspection needs efficiently and consistently.
2Productivity
If radiation-based systems are used for rapid imaging, then inspection speed increases, but the ability to accurately detect contraband remains limited due to superimposed images
Solution Approach 1:
The patent applies preliminary processing techniques to images before analysis, including de-superimposition algorithms and image enhancement methods. These preliminary actions prepare the images by separating overlapping cargo layers and enhancing contrast, making contraband more visible and detectable while preserving the rapid inspection capability of radiation-based systems.
Solution Approach 2:
The system changes image parameters through digital processing, adjusting contrast, brightness, and structural information to highlight potential threats. By modifying these parameters, the system improves the visibility of contraband within superimposed images without requiring slower physical inspection methods.
3Adaptability or versatility
If multiple AI models from different third-party platforms are integrated, then analytical capabilities expand, but system complexity increases
Solution Approach 1:
The patent implements a universal integration platform that can accommodate multiple AI models from different third-party sources through standardized interfaces. This multi-functional system allows various analytical models to be deployed and managed through a common architecture, expanding capabilities while maintaining manageable system complexity through standardization.
Solution Approach 2:
The system introduces an intermediary integration layer that mediates between diverse AI models and the inspection system. This intermediary component handles model deployment, data formatting, and result aggregation, allowing multiple third-party AI platforms to be integrated without directly complicating the core inspection architecture.
4Productivity
If automated systems are implemented to determine cargo compliance, then inspection efficiency improves, but the ability to handle complex manifest data integration remains challenging
Solution Approach 1:
The patent segments the complex data integration process into distinct modules: manifest data parsing, image data processing, comparison logic, and result generation. By dividing the integration task into separate functional segments, the system handles complex manifest data more efficiently while maintaining manageable complexity through modular architecture.
Data Source
AI summary
A system for enabling a plurality of different artificial intelligence models to be applied to image data acquired from one or more inspection systems. The system is configured to receive an instruction from an operator workstation to apply one of a plurality of different artificial intelligence (AI) models to an image representative of cargo. Further, an application program interface specific to that one AI model, is acquired. Further, the image is submitted to the AI model using the application program interface and data representative of a degree to which the cargo in the image corresponds to an expected type of cargo based upon descriptive data associated with the cargo, is received from the AI model. Thereafter, the data representative of the degree to which the cargo in the image corresponds to the expected type of cargo is transmitted to the operator workstation, to be displayed to an operator.