Material Composition Identification Using Image Recognition
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Solution Overview
Problem
Current methods for identifying material composition, such as manual identification and laboratory inspection, are prone to human error and inefficient, especially when dealing with mixed recyclable materials, leading to low accuracy and prolonged identification times.
Innovation Solution
A computer-implemented method that involves acquiring a target image, determining a material cross-sectional image, generating a material composition area image set, and using image recognition to determine the material composition information, improving both accuracy and efficiency through pre-processing and the use of pre-trained models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification is used for material composition, then human judgment can be applied, but accuracy is low due to human subjective factors
Solution Approach 1:
The patent replaces manual mechanical identification with an automated image recognition system using deep learning models. The system captures images of materials, processes them through pre-trained neural networks, and automatically identifies material composition, eliminating human subjective factors and improving both accuracy and consistency.
Solution Approach 2:
The patent creates a digital copy (image) of the physical material and uses this copy for analysis instead of direct manual inspection. The image recognition system analyzes the visual characteristics of the material copy to determine composition, providing consistent and repeatable results without human intervention.
2Measurement precision
If multiple inspection methods are used for mixed recyclable materials, then identification completeness improves, but time consumption increases
Solution Approach 1:
The patent merges multiple inspection methods into a single integrated image recognition system. By combining various detection capabilities into one unified deep learning model, the system achieves comprehensive material identification without requiring sequential application of multiple separate inspection methods, thus reducing time consumption while maintaining completeness.
Solution Approach 2:
The patent creates a universal image recognition system that can handle multiple types of materials and inspection tasks simultaneously. The pre-trained deep learning model is designed to identify various material compositions (plastics, metals, composites) through a single imaging and processing workflow, eliminating the need for specialized procedures for each material type.
3Measurement precision
If comprehensive material inspection is performed, then identification accuracy improves, but processing complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training deep learning models with extensive material data before actual inspection. The pre-trained models already contain learned features and patterns for identifying various material compositions, so during actual use, the system can comprehensively analyze materials without requiring complex real-time processing, as the heavy computational work was done in advance during model training.
Data Source
AI summary
Computer-implemented methods for determining the material composition of an object are disclosed. The method may include obtaining a target image of the object; determining a material cross-sectional image corresponding to a material to be detected and included in the target image; determining a most similar candidate image to the target image and corresponding candidate material composition information, where the most similar candidate image is selected from a target database; generating a material composition area image set comprising a plurality of material composition area images associated with the material cross-sectional image and the most similar candidate image; determining a set of material composition information corresponding to each of the plurality of material composition area images in the material composition area image set; and obtaining a material composition information set from the set of material composition information. This method improves the recognition accuracy and recognition efficiency of the material composition.