Image Processing Device Classification Accuracy via Goodness of Fit
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Solution Overview
Problem
Existing image processing devices face difficulties in accurately classifying diverse material component images using pre-trained machine learning models, as maintaining the latest model state is challenging, and current technologies do not effectively handle images that are hard to classify.
Innovation Solution
An image processing device that acquires and classifies material component images, computes a goodness of fit for classification results, and transmits difficult-to-classify images to a data management device for further processing, where a second classification section uses a more advanced trained model to return accurate classification results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-trained machine learning model is installed in the image processing device, then classification processing can be performed locally, but the device cannot accurately classify diverse material component images when the model is not updated to the latest state
Solution Approach 1:
The patent introduces a data management device as an intermediary between the image processing device and the latest model sources. This intermediary receives images that were difficult to classify, performs re-classification using updated models, and returns results to the image processing device, thereby resolving the contradiction between local processing capability and adaptability to diverse images
Solution Approach 2:
The system performs preliminary classification locally using the installed model, then identifies and transmits only the difficult-to-classify images for further processing. This preliminary action allows the system to handle most images efficiently while ensuring accurate classification of challenging cases through subsequent re-classification
2Reliability
If all material component images are transmitted to the data management device for re-classification, then classification accuracy improves, but data transmission volume and processing time increase
Solution Approach 1:
The patent applies local quality by evaluating each image's classification confidence individually and selectively transmitting only those images with low confidence scores (difficult-to-classify images). This approach ensures high classification accuracy for problematic images while minimizing data transmission volume and processing time for the overall system
3Reliability
If the trained model is updated frequently to maintain the latest state, then classification accuracy for diverse images improves, but the complexity of maintaining the model increases
Solution Approach 1:
The data management device serves as an intermediary that handles model updates centrally. Instead of requiring the image processing device to frequently update its installed model, the intermediary manages model versions and provides re-classification services, thereby reducing the maintenance complexity at the image processing device while ensuring access to latest classification capabilities
Data Source
AI summary
An image processing device including an acquisition section that acquires plural material component images, a first classification section that classifies the plural material component images as detected components for respective prescribed classifications and computes a goodness of fit for classification results, and a transmission section that, based on the computed goodness of fit, transmits designated material component images from among the plural material component images via a network line to a data management device.