Convolutional Neural Network for Fragmented Material Image Classification
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Solution Overview
Problem
Current image processing techniques face challenges in accurately identifying fragmented material portions, particularly in irregularly shaped materials like rock, due to the lack of common features for classification.
Innovation Solution
A method utilizing a convolutional neural network with multiple layers, including convolution, pooling, and up-sampling layers, trained on images of fragmented materials to classify pixels as edges, inward from edges, or interstices, and applying morphological algorithms to associate edges with material portions, incorporating pixel depth information and superpixel representation for improved classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation techniques are used to classify fragmented portions, then the processing method is simple, but the classification accuracy is low due to irregular shapes and lack of common features
Solution Approach 1:
The patent replaces conventional image segmentation algorithms with a convolutional neural network (CNN) system. The CNN automatically learns hierarchical features from pixel data through multiple convolutional layers, pooling layers, and fully connected layers, eliminating the need for manual feature engineering and segmentation parameter tuning. This substitution of mechanical processing with intelligent learning significantly improves classification accuracy for irregular fragmented materials.
Solution Approach 2:
The patent implements extensive preliminary actions including: (1) collecting and annotating large datasets of fragmented materials with various shapes and sizes, (2) preprocessing images to enhance features and reduce noise, (3) designing and training the CNN architecture with multiple layers and activation functions, and (4) performing cross-validation and hyperparameter optimization. These preliminary actions establish a robust foundation that enables accurate classification without requiring complex processing during actual operation.
2Measurement precision
If detailed pixel-level classification is performed to accurately identify fragmented portions, then the classification precision is improved, but the computational time increases
Solution Approach 1:
The patent applies segmentation at multiple levels: (1) The image is divided into patches or regions of interest that are processed independently, (2) The CNN architecture segments features hierarchically through multiple layers, with early layers detecting edges and textures, and deeper layers identifying complete fragmented portions. This multi-level segmentation allows parallel processing and reduces the computational burden on any single processing unit, maintaining high accuracy while reducing overall processing time.
Solution Approach 2:
The patent implements partial processing strategies including: (1) Processing only relevant regions of the image rather than all pixels uniformly, (2) Using early stopping during training when validation accuracy plateaus, (3) Applying thresholding to classify only ambiguous cases that require detailed analysis. These partial action approaches maintain classification accuracy for critical cases while reducing unnecessary computational effort on already-clear regions.
3Measurement precision
If multiple convolutional layers and processing steps are used to improve classification, then the identification accuracy is enhanced, but the device complexity increases
Solution Approach 1:
The patent implements a nested architecture where convolutional layers are embedded within a hierarchical structure: convolutional layers extract local features, pooling layers summarize regional information, and fully connected layers integrate global context. Each layer is nested within the previous one, with features flowing from simple to complex representations. This nested organization allows the system to achieve high identification accuracy through multiple processing stages while maintaining a structured and manageable architecture.
4Adaptability or versatility
If training data includes various lighting conditions and angles to improve robustness, then the adaptability is improved, but the data processing complexity increases
Solution Approach 1:
The patent creates a universal training dataset that incorporates multiple lighting conditions, camera angles, distances, and material types. The CNN architecture is designed to be multi-functional, learning to extract relevant features regardless of the specific imaging conditions. This universal approach allows the model to adapt to new conditions without requiring condition-specific processing pipelines, reducing the complexity of data preprocessing while maintaining high robustness across varying environments.
Data Source
AI summary
A method and apparatus for processing an image of fragmented material to identify fragmented material portions within the image is disclosed. The method involves receiving pixel data associated with an input plurality of pixels representing the image of the fragmented material. The method also involves processing the pixel data using a convolutional neural network, the convolutional neural network having a plurality of layers and producing a pixel classification output indicating whether pixels in the input plurality of pixels are located at one of an edge of a fragmented material portion, inwardly from the edge, and at interstices between fragmented material portions. The convolutional neural network includes at least one convolution layer configured to produce a convolution of the input plurality of pixels, the convolutional neural network having been previously trained using a plurality of training images including previously identified fragmented material portions. The method further involves processing the pixel classification output to associate identified edges with fragmented material portions.