Transfer Learning CNN for X-ray Threat Detection
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Solution Overview
Problem
Existing image processing systems for x-ray security screening face challenges in accurately identifying specific items and threats due to limited availability of training data, requiring large datasets for deep learning convolutional neural networks (CNNs), which is unobtainable in many application domains.
Innovation Solution
The implementation of a deep learning CNN framework that employs transfer learning, retraining a pre-trained CNN with a smaller dataset of x-ray images to identify specific objects and threats, using a machine learning algorithm trained on a specific type of images, such as x-ray volumetric or projection images, to analyze input images and generate models for threat detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep multi-layer CNN approach is used for image classification, then detection accuracy is improved, but the requirement for large amounts of training data increases
Solution Approach 1:
The patent applies transfer learning by pre-training a CNN on a large dataset (ImageNet) before fine-tuning it on the specific x-ray security imaging dataset. This preliminary training action allows the model to learn general feature representations that can be transferred to the target domain, thereby achieving high detection accuracy with limited domain-specific training data
Solution Approach 2:
The patent modifies the pre-trained CNN model by changing its parameters (weights and biases) through fine-tuning on the specific security imaging dataset. This parameter adjustment allows the model to adapt from general image recognition to specific threat detection while requiring fewer training samples
2Adaptability or versatility
If transfer learning is employed to overcome limited training data, then the system can be optimized for specific application domains, but the overall system complexity increases
Solution Approach 1:
The patent segments the training process into two distinct phases: pre-training on a general dataset and fine-tuning on the specific security imaging dataset. This segmentation allows the system to leverage pre-trained features while adapting to domain-specific characteristics, achieving adaptability without requiring complete retraining from scratch
3Measurement precision
If a pre-trained CNN is fine-tuned for specific application domains, then detection accuracy for specific items is improved, but the training time and computational resources increase
Solution Approach 1:
By performing pre-training on a large dataset before fine-tuning, the model already possesses robust feature extraction capabilities. This preliminary action reduces the fine-tuning time required compared to training a model from scratch, as the model only needs to adapt its parameters to the specific domain rather than learning features from zero
Solution Approach 2:
The patent applies fine-tuning selectively to the later layers of the CNN architecture that are most critical for specific classification tasks, while keeping earlier feature extraction layers fixed or minimally adjusted. This localized adjustment approach reduces computational overhead and training time while maintaining high detection accuracy for specific items
Data Source
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AI summary
A computing-device implemented system and method for identifying an item in an x-ray image is described. The method includes training a machine learning algorithm with at least one training data set of x-ray images to generate at least one machine- learned model. The method further includes receiving at least one rendered x-ray image that includes an item, identifying the item using the at least one model, and generating an automated detection indication associated with the item.