Customs Inspection System Using HSCODE-Based Image Segmentation
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Solution Overview
Problem
Current intelligent security inspection systems for customs declarations face challenges in handling severe non-rigid deformations, perspective superimpositions, real-time processing of multiple categories, and inconsistencies between devices, leading to ineffective identification of false or concealed declarations.
Innovation Solution
A method and system utilizing supervised image segmentation and feature extraction to identify regions of interest in images based on HSCODE models, with a hierarchical structure and dual model policy (local and cloud models) for accurate comparison and self-learning, incorporating multi-scale frequency domain features and adaptive similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image matching algorithms are used for customs declaration comparison, then the system can perform basic image comparison, but it fails to handle severe non-rigid deformations and perspective superimpositions effectively
Solution Approach 1:
The patent divides the container image into multiple small local patches and compares each patch with corresponding patches from reference images. This local segmentation approach allows the system to handle non-rigid deformations and perspective superimpositions by processing manageable local regions independently, rather than attempting to match the entire deformed image at once.
Solution Approach 2:
The patent transforms the 2D image matching problem into a 3D feature space by extracting multi-dimensional features (color histograms, texture features, gradient orientations) from local patches. This dimensional transformation enables more robust comparison by capturing object characteristics from multiple attribute dimensions, making the matching insensitive to geometric deformations in the original 2D space.
2Productivity
If big-data inference and image classification algorithms are used for analyzing customs declarations, then the system can process large datasets, but the effect is limited when there are a large number of categories
Solution Approach 1:
The patent segments the large-scale category classification problem into multiple local patch comparisons. Each patch is independently analyzed and matched against reference databases, allowing parallel processing of numerous categories without overwhelming the system. This segmentation enables scalable handling of large category numbers while maintaining detection accuracy through localized analysis.
Solution Approach 2:
The patent performs comparison on multiple local patches within each image, applying partial actions to different regions. By aggregating results from multiple partial comparisons across numerous patches, the system achieves comprehensive classification accuracy for large numbers of categories while maintaining efficient parallel processing throughput.
3Ease of operation
If conventional solutions are used for customs declaration comparison, then the system can perform basic inspection, but it cannot satisfy user requirements due to inconsistencies between devices and regional differences
Solution Approach 1:
The patent extracts and compares multiple parameters (color histograms, texture features, gradient orientations) from local patches rather than relying on single global image parameters. This multi-parameter approach compensates for device-specific variations and regional differences, as the combined parameter set provides a more robust and consistent representation across different imaging conditions and locations.
Solution Approach 2:
The patent develops a universal comparison framework that processes images from different devices and regions using the same local patch analysis and multi-parameter extraction methodology. This universal approach ensures consistent detection performance across diverse devices and regional offices, making the system adaptable to various imaging conditions while maintaining standardized inspection quality.
4Productivity
If simple image comparison methods are used, then the system can process images quickly, but it cannot accurately identify false or concealed declarations in complex scenarios
Solution Approach 1:
The patent segments the image into multiple local patches that can be processed in parallel, enabling real-time processing speed through distributed computation. Each small patch requires less computational resources than the full image, allowing rapid processing while the aggregation of multiple patch results maintains high verification accuracy for complex declaration scenarios.
Solution Approach 2:
The patent enhances measurement precision by transforming simple 2D image comparison into multi-dimensional feature space analysis. By extracting and comparing color, texture, and gradient features from local patches, the system achieves accurate declaration verification that simple pixel-level comparison cannot provide, while maintaining real-time processing through efficient feature extraction algorithms.
Data Source
AI summary
The present disclosure provides a method and a system for inspecting goods. The method includes the steps of: obtaining a transmission image and a HSCODE of inspected goods; processing the transmission image to obtain a region of interest; retrieving from a model library a model created based on the HSCODE, in accordance with the HSCODE of the inspected goods; and determining whether there are any goods not registered in a customs declaration that are contained in the region of interest based on the model. With the above solution, it is possible to inspect goods in a container efficiently, so as to find out whether there are goods not indicated in the customs declaration that are concealed in the container.