Synthetic X-Ray Scan Generation for Multi-Orientation Cargo Objects
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Solution Overview
Problem
Existing X-ray scanning systems face bottlenecks in high-throughput scanning due to manual image adjudication time and insufficient training data, particularly in cargo and vehicle inspection where objects can be oriented in various ways, complicating the generation of synthetic X-ray scan data.
Innovation Solution
A system and method for automatically generating synthetic X-ray scan data of objects in multiple orientations using a frame, robotic arms, and a computing device to control movements, capturing and adjusting scan data to simulate various orientations and conditions, and integrating it into cargo container scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to generate synthetic X-ray scan data by physically manipulating objects into different orientations and rescanning, then the data can be captured, but the process becomes prohibitively time-consuming and labor-intensive
Solution Approach 1:
The patent uses 3D computer-generated models as virtual copies of physical objects to generate synthetic X-ray scan data. Instead of physically manipulating real objects through multiple orientations and scanning each position, the system creates digital twins of objects and computationally generates X-ray images from these models, dramatically reducing the time and labor required while maintaining data quality
Solution Approach 2:
The patent replaces the mechanical system of physical object manipulation and physical scanning with a computational system. Rather than using mechanical devices to rotate and position physical objects before scanning, the system uses computer algorithms to simulate X-ray projection through 3D models, substituting mechanical operations with digital processing
2Adaptability or versatility
If the number of orientations and clutter types are increased to improve training data diversity, then the quality of machine learning training improves, but the complexity and time required to capture all combinations increases significantly
Solution Approach 1:
The patent implements dynamic generation of synthetic data by allowing flexible modification of object orientations, clutter types, and scanning parameters through software control. The system can dynamically adjust training data characteristics without requiring physical reconfiguration of the scanning setup, enabling diverse training scenarios while maintaining system simplicity
Solution Approach 2:
The patent changes key parameters such as object orientation angles, clutter material types, and X-ray energy levels through computational adjustments rather than physical modifications. By varying these parameters in the 3D modeling and image synthesis process, the system generates diverse training data without increasing physical system complexity
3Reliability
If more real-world scan data are collected to improve training, then the model performance improves, but the availability of shareable data is limited by customs authority restrictions
Solution Approach 1:
The patent creates synthetic copies of real-world X-ray scan scenarios through 3D modeling and computational image generation. These synthetic datasets replicate the characteristics of authentic cargo container scans including various objects, orientations, and clutter types, providing unlimited shareable training data without requiring access to restricted real-world customs data
Solution Approach 2:
The patent introduces synthetic data as an intermediary between the need for diverse training data and the unavailability of real-world shareable data. This intermediary approach allows training data to be generated without directly using or sharing sensitive real-world customs scan data, bridging the gap between data availability requirements and security restrictions
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
Enables fully automated generation of synthetic X-ray scan data for training machine learning algorithms, reducing manual intervention and improving the efficiency and accuracy of object detection in X-ray images.
Implementation Method 1
an X-ray source for generating an X-ray beam that impinges on the frame and a detector array for capturing resultant X-ray scan data
Data Source
AI summary
Systems and methods of automatically generating synthetic X-ray scan data include generating scan data corresponding to a frame holding or supporting an object, wherein the frame and hence the object is manipulated, without manual intervention, to be positioned in a plurality of orientations in three dimensional space. Subsequently, X-ray scan data corresponding to the object is isolated and extracted from the X-ray scan data corresponding to the frame, the X-ray scan data corresponding to the object is adjusted and finally each of the adjusted X-ray scan data corresponding to the object is inserted into X-ray scan data of a cargo container in order to generate a plurality of X-ray scan data of the cargo container embedded with the object.