Cargo Recognition via Image Segmentation and Texture Analysis
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Solution Overview
Problem
Current automatic classification and recognition technologies for container cargoes face challenges due to cargo diversity and complexity, limited image data for training, and high requirements for algorithms and computing hardware, with existing methods like X-ray dual energy and neutron-X-ray technologies having limitations in penetration and cost.
Innovation Solution
A fluoroscopic inspection method using X-ray scanning to segment images into regions with similar gray scales and texture, extracting features, generating a classifier through annotated images, and recognizing regions to estimate cargo quantities and categories, employing techniques like SIFT, MR8, and Dictionary learning for effective classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If X-ray dual energy technology is used for material recognition, then the recognition capability for organic matters, inorganic matters, mixtures, and heavy metals is achieved, but the recognition range is narrow and cannot effectively classify diversified cargoes
Solution Approach 1:
The scanned image is segmented into multiple small regions based on gray scale and texture features. Each region is independently classified by the trained classifier, enabling detailed cargo classification. This segmentation approach transforms the inability to handle diverse cargoes into a strength by allowing region-specific analysis.
Solution Approach 2:
The patent changes the classification parameters from material composition (organic/inorganic) to visual features (gray scale, texture). By training the classifier on visual characteristics rather than material properties, the system achieves broader adaptability across different cargo types while maintaining recognition precision.
2Adaptability or versatility
If neutron-X-ray technology is used for wide range cargo recognition, then the recognition range is expanded, but the equipment cost increases significantly and protection becomes difficult
Solution Approach 1:
Instead of using expensive neutron-X-ray equipment, the patent creates a virtual classification system that copies the functionality of advanced material recognition through image processing. The trained classifier acts as a software copy that replicates cargo identification capabilities without requiring expensive hardware.
Solution Approach 2:
The patent replaces the mechanical/neutron-based physical inspection system with an information-processing system. By substituting physical neutron interaction with digital image analysis and classification algorithms, the system achieves similar recognition capabilities without the associated cost and complexity.
3Measurement precision
If sufficient image data is collected for training the classifier, then the classification accuracy is improved, but the data acquisition becomes difficult due to distributed scanning devices and data secrecy
Solution Approach 1:
The training data structure is designed to be universal and adaptable. The classifier can be trained on data from any scanning device position and applied to classify cargoes across different locations. This universality allows the system to achieve high accuracy without requiring extensive data collection from each specific device.
Solution Approach 2:
The patent performs preliminary data preparation and classifier training in advance. By pre-training the classifier with available data and preparing the classification framework beforehand, the system reduces the need for extensive real-time data collection and processing, overcoming data acquisition difficulties.
4Adaptability or versatility
If advanced algorithms and mass data analysis are used for cargo classification, then the classification capability is enhanced, but the requirements for computing hardware and algorithms increase significantly
Solution Approach 1:
By segmenting the image into small regions, the patent reduces the computational complexity of classifying entire large images. Each small region can be independently and efficiently classified, dividing the heavy computational task into manageable units that require fewer resources.
Solution Approach 2:
The patent simplifies the classification parameters to focus on key visual features (gray scale, texture) rather than analyzing all possible cargo attributes. This parameter reduction maintains classification capability while significantly lowering the computational requirements for algorithm execution.
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
The method improves the accuracy and efficiency of cargo classification and recognition, enabling the detection of illegally smuggled goods and estimation of cargo quantities, while reducing the need for extensive image data and computational resources.
Implementation Method 1
performing scanning and imaging for a container by using an X-ray scanning device to acquire a scanned image
Data Source
AI summary
The present disclosure relates to a fluoroscopic inspection system for automatic classification and recognition of cargoes. The system includes: an image data acquiring unit, configured to perform scanning and imaging for a container by using an X-ray scanning device to acquire a scanned image; an image segmenting unit, configured to segment the scanned image into small regions each having similar gray scales and texture features; a feature extracting unit, configured to extract features of the small regions; a training unit, configured to generate a classifier according to annotated images; and a classification and recognition unit, configured to recognize the small regions by using the classifier according to the extracted features, to obtain a probability of each small region pertaining to a certain category of cargoes, and merge small regions to obtain large regions each representing a category.