Coating Region Evaluation Using AI Image Segmentation
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Solution Overview
Problem
Existing methods for evaluating the covering of an object require manual intervention and are prone to inaccuracies due to reliance on human judgment and time-consuming threshold setting.
Innovation Solution
An evaluation system utilizing a trained model to automatically identify the coating region of an object from an image and calculate an evaluation value, leveraging machine learning techniques such as Mask R-CNN for accurate and efficient assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to evaluate coating regions, then human judgment can be applied, but the evaluation process becomes time-consuming and prone to inaccuracies
Solution Approach 1:
The patent replaces manual visual inspection and mechanical evaluation methods with an automated image processing system using trained models (neural networks). The system automatically identifies coating regions, calculates coverage ratios, and generates evaluation results without human intervention, thereby eliminating time loss and improving measurement precision through consistent algorithmic application.
Solution Approach 2:
The evaluation system performs self-service by automatically processing images through trained models to identify coating regions and calculate evaluation metrics. The system independently completes the entire evaluation workflow from image input to result generation without requiring manual threshold setting or human judgment, thus resolving the contradiction between speed and accuracy.
2Adaptability or versatility
If manual threshold setting is used for coating region identification, then flexibility in adjustment is achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The patent replaces manual threshold setting with trained machine learning models that automatically adapt to different coating scenarios. The models learn optimal threshold values during training and apply them automatically during evaluation, eliminating the need for manual adjustment while maintaining flexibility across different applications. This reduces process complexity significantly.
Solution Approach 2:
The system performs preliminary action by pre-training models on diverse datasets before actual evaluation. This preliminary training phase captures the adaptability needed for various coating types and conditions, so that during actual use, the system can directly apply learned knowledge without requiring manual threshold adjustment, thus simplifying the evaluation process while maintaining versatility.
3Productivity
If automated image processing is implemented, then evaluation speed increases, but accuracy may deteriorate without proper training data
Solution Approach 1:
The patent applies preliminary action by extensively training the machine learning models on large, diverse datasets before deployment. This preliminary training ensures that the models learn accurate patterns for identifying coating regions across various conditions. When deployed, the pre-trained models maintain high accuracy while providing fast automated evaluation, thus resolving the contradiction between speed and precision.
Solution Approach 2:
The system incorporates feedback mechanisms where model predictions are continuously refined based on performance metrics and comparison with ground truth data. This feedback loop ensures that the automated processing maintains high accuracy by learning from errors and improving over time, while preserving the speed benefits of automation.
Data Source
AI summary
An evaluation system includes at least one processor. The at least one processor is configured to: acquire a target image showing an object having a base material and a coating region on the base material; input the target image to a trained model estimating the coating region from an input image to identify the coating region of the object; and calculate an evaluation value related to the identified coating region.