Firearm Detection Neural Network for X-ray Inspection
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Solution Overview
Problem
Current radiation inspection technologies for detecting firearms in containers or vehicles face challenges such as high workload for security personnel, visual fatigue, and inability to operate 24/7 due to manual inspection methods, as well as difficulties in feature recognition in complex backgrounds using conventional computer vision techniques.
Innovation Solution
A method and device utilizing a trained firearm detection neural network for X-ray inspection, which determines candidate regions and classifies them to detect firearms, with the ability to automatically calculate confidence levels and mark firearm positions, employing convolutional neural networks and random jitter processing for enhanced generalization and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection by security personnel is used, then inspection can be performed with simple equipment, but workload is enormous and visual fatigue causes missed or erroneous inspection
Solution Approach 1:
The system enables self-service inspection through automated neural network detection. The firearm detection neural network automatically analyzes transmission images, identifies candidate regions, and determines whether firearms are present without requiring security personnel to manually examine each image, thereby eliminating visual fatigue and human error while maintaining simple equipment requirements
Solution Approach 2:
The patent replaces the mechanical human inspection process with an automated computational system. The neural network model substitutes human visual analysis with algorithmic image processing, where the processor automatically performs feature extraction, candidate region determination, and firearm classification based on transmission images from the radiation imaging device
2Productivity
If manual inspection is used, then equipment complexity is low, but inspection cannot operate 24 hours affecting timeliness
Solution Approach 1:
The system achieves continuous 24-hour operation through automated processing. The neural network model can continuously analyze transmission images without interruption, eliminating the need for human rest periods. The processor automatically processes images as they are generated by the radiation imaging device, ensuring uninterrupted inspection capability while maintaining relatively simple equipment architecture
Solution Approach 2:
The automated system performs self-service inspection without human intervention. The neural network model independently executes the entire inspection process from image analysis to firearm detection, enabling the system to operate autonomously around the clock without requiring human operators for each inspection cycle
3Reliability
If conventional feature extraction methods are used, then device complexity is low, but recognition fails in complex and ever-changing backgrounds
Solution Approach 1:
The patent transforms the feature extraction approach by changing from hand-crafted features to learned features through neural networks. The firearm detection neural network automatically learns optimal features from training data, adapting to various backgrounds and firearm orientations. This parameter change in the feature representation method significantly improves recognition accuracy in complex backgrounds while the system maintains practical complexity through efficient implementation
4Adaptability or versatility
If multiple features are combined in conventional methods, then some generalization is achieved, but it is still very difficult to design a good feature and achieve generalization
Solution Approach 1:
The patent replaces the manual feature design process with automated feature learning through neural networks. Instead of security personnel or engineers manually designing and combining multiple features, the firearm detection neural network automatically learns discriminative features from training data. This substitution eliminates the difficulty of feature design while achieving superior generalization across different backgrounds and scenarios
Solution Approach 2:
The system changes from static hand-crafted features to dynamic learned features. The neural network adapts its feature extraction parameters based on training data, automatically adjusting to different backgrounds, lighting conditions, and firearm types. This parameter transformation enables the system to achieve broad generalization without manual feature engineering
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
This solution enables accurate and automated detection of firearms in radiation images, reducing the workload for security personnel and allowing for continuous operation, while improving recognition accuracy and handling complex backgrounds through deep learning algorithms.
Implementation Method 1
Radiation imaging is a technology which achieves observation of the interior of an object by transmitting high-energy rays through the object
Data Source
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AI summary
An inspection device and a method for detecting a firearm are disclosed. X-ray inspection is performed on an inspected object to obtain a transmission image. A plurality of candidate regions in the transmission image are determined using a trained firearm detection neural network. The plurality of candidate regions are classified using the firearm detection neural network to determine whether there is a firearm included in the transmission image. With the above solution, it is possible to determine more accurately whether there is a firearm included in a container/vehicle.