Waste Pit Image Analysis for Material Identification
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Solution Overview
Problem
Current methods for identifying waste material types in waste treatment plants, such as JP 2007-126246 A and JP 2015-124955 A, face challenges in accurately distinguishing between different types of waste based on color tone and brightness values, leading to potential combustion issues and equipment troubles due to incorrect identification of foreign waste materials.
Innovation Solution
An information processing device and method that uses machine learning to identify waste material types by analyzing image data from the waste pit, employing an identification algorithm that differentiates between various types of waste and non-waste targets, enabling precise control of waste agitation and combustion processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If waste material types are identified by color tone (as in JP 2007-126246 A), then the identification process is simple, but the identification accuracy is insufficient leading to potential combustion issues
Solution Approach 1:
The patent changes the identification parameters from simple color tone to multiple parameters including color tone, brightness, and texture features. This allows for more accurate differentiation of waste material types while maintaining a relatively simple image-based identification process.
Solution Approach 2:
The patent adds texture analysis as an additional dimension to the identification process. By combining color tone (2D RGB values) with texture features (spatial frequency domain information), the system achieves better identification accuracy without significantly increasing system complexity.
2Device complexity
If waste material types are identified by brightness value (as in JP 2015-124955 A), then the detection method is simple, but it cannot accurately distinguish between different types of waste material
Solution Approach 1:
The patent extends the identification from single-parameter brightness to multi-parameter analysis including brightness, color tone, and texture. This allows the system to differentiate between waste materials with similar brightness values but different textures or colors.
Solution Approach 2:
The patent segments the image analysis into multiple feature extraction stages: color feature extraction, brightness feature extraction, and texture feature extraction. This segmented approach allows each parameter to contribute to the overall identification accuracy while keeping individual processing steps relatively simple.
3Measurement precision
If machine learning-based identification is implemented, then waste material identification accuracy is significantly improved, but the device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary feature extraction and preprocessing of image data before feeding it to the machine learning model. By pre-processing the images to extract relevant features (color, brightness, texture) and organizing them in a structured format, the system reduces the computational burden on the machine learning model and speeds up the identification process.
Solution Approach 2:
The patent uses a pre-trained machine learning model that has been trained on a large dataset of waste material images. This pre-trained model can be deployed as-is or fine-tuned with minimal additional training data, reducing the complexity of model development and deployment while maintaining high identification accuracy.
Data Source
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AI summary
Provided is an information processing device capable of identifying a type of waste material in a waste pit. The information processing device includes a type identification unit having an identification algorithm generated by learning training data in which the type of the waste material is labeled on past image data obtained by capturing the inside of the waste pit where the waste material is accumulated, and configured to identify the type of the waste material accumulated in the waste pit when new image data obtained by capturing the inside of the waste pit is given as an input.