Large-Truck X-Ray Inspection Using Neural Template Comparison
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Solution Overview
Problem
Existing vehicle inspection methods, particularly for large trucks, suffer from inefficiencies and inaccuracies in detecting suspicious items due to reliance on traditional image processing techniques, which are prone to false alarms and missed detections, especially when dealing with complex and varied cargo configurations.
Innovation Solution
A method and system utilizing a vehicle template library constructed through convolutional neural networks for feature extraction and clustering, combined with a Siamese metric network for accurate difference detection between inspected and template images, to identify variation regions and present them to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing techniques are used for vehicle inspection, then the system is simple to implement, but the detection accuracy is low with high false alarm rates
Solution Approach 1:
The patent introduces a template library as an intermediary between the input image and the detection result. The template library stores pre-processed vehicle images that serve as reference standards, enabling accurate comparison and reducing false alarms without requiring complex real-time processing algorithms.
Solution Approach 2:
The patent performs preliminary actions by pre-processing images to create a template library before actual inspection. This includes selecting representative vehicle images, processing them to remove background and standardize formatting, and storing them for quick comparison during inspection, thereby improving detection accuracy without increasing real-time complexity.
2Measurement precision
If traditional image processing techniques are used, then the processing speed is fast, but the detection accuracy is low leading to missed detections
Solution Approach 1:
The template library is prepared in advance through image preprocessing, including selecting representative vehicles, cropping to remove backgrounds, and standardizing formats. This preliminary work enables fast comparison during actual inspection while maintaining high detection accuracy.
Solution Approach 2:
The patent creates copies of standard vehicle images in the template library that can be quickly compared against inspection images. These pre-processed templates serve as reference copies that enable rapid detection without requiring complex real-time analysis, thus maintaining productivity while improving accuracy.
3Measurement precision
If existing target detection algorithms are applied to large truck inspection, then the approach is straightforward, but the detection results are not ideal
Solution Approach 1:
The patent applies local quality by focusing detection on specific regions of interest within the vehicle cargo area. Rather than attempting to detect all objects uniformly, the system identifies and focuses on suspicious regions by comparing them against templates, thereby improving detection accuracy for critical areas while maintaining overall system efficiency.
Solution Approach 2:
The patent changes key parameters by transforming the inspection approach from general object detection to template-based difference detection. This involves changing the detection metric from identifying objects to identifying deviations from normal vehicle cargo patterns, which significantly improves adaptability to different vehicle types and cargo configurations.
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
Improves identification accuracy and real-time performance of vehicle inspections by reducing false detections and optimizing template matching, addressing the inefficiencies and inaccuracies of traditional methods.
Implementation Method 1
performing X-ray radiation imaging on a vehicle and generating an X-ray radiation image of the whole vehicle
Data Source
AI summary
A method of inspecting a vehicle includes: acquiring a to-be-inspected image of an inspected vehicle (S11); acquiring a visual feature of the to-be-inspected image using a first neural network model (S12); retrieving a template image from a vehicle template library based on the visual feature of the to-be-inspected image (S13); determining a variation region between the to-be-inspected image and the template image (S14); and presenting the variation region to a user (S15). The system of inspecting a vehicle includes a radiation imaging device (150), a display device (130), an image processor (140), and a storage device (120). The present disclosure further includes a computer-readable storage medium.