AI-Based Battery Defect Detection Using CT Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing battery inspection systems rely on traditional image processing, which can be inconsistent, inefficient, and inaccurate, especially when dealing with poor-quality images or different types of batteries.
Innovation Solution
The implementation of an intelligent defect recognition system utilizing AI models that can identify, classify, localize, and quantify battery defects from CT images, including the use of AI-based image denoising and upscaling filters to handle low-quality images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing is used for battery inspection, then the system is simpler to implement, but the inspection consistency and accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical image processing systems with an AI-based inspection system. The AI model (neural network) automatically analyzes CT images to detect defects, replacing manual or rule-based processing. This substitution enables higher accuracy and consistency while handling complex image data that traditional methods struggle with, such as poor-quality images or subtle defects.
Solution Approach 2:
The system changes the processing parameters by using deep learning models that can adapt to different image qualities and defect types. The AI model learns from training data and automatically adjusts its detection criteria, allowing it to maintain high accuracy across varying inspection conditions without requiring manual reconfiguration of processing parameters.
2Productivity
If traditional image processing is used, then the system is less computationally intensive, but the inspection time and efficiency deteriorate
Solution Approach 1:
The system performs preliminary action by pre-training AI models on extensive datasets of battery images before actual inspection. This pre-training enables the model to quickly and accurately detect defects during production without requiring intensive real-time computation. The heavy computational work is done beforehand during model training, not during inspection.
Solution Approach 2:
The AI inspection system is self-service in that it automatically processes images without requiring manual intervention or complex post-processing steps. The model independently analyzes images, identifies defects, and provides results, eliminating the need for human operators to manually review each image or apply multiple processing algorithms sequentially.
3Measurement precision
If traditional image processing is used, then the system is more robust to poor-quality images, but the detection accuracy for defects deteriorates
Solution Approach 1:
The system uses feedback mechanisms where the AI model continuously learns from inspection results and adjusts its detection criteria. The model can identify patterns in poor-quality images and adapt its analysis to compensate for image degradation. This feedback loop enables the system to maintain high detection accuracy even when input images are of poor quality, as long as the model has been trained on similar conditions.
Solution Approach 2:
The AI model performs preliminary learning from high-quality training images before inspecting actual battery images. During pre-training, the model learns to recognize defect patterns and understand image quality variations, enabling it to compensate for poor-quality images during production inspections. This preliminary education allows the model to maintain accuracy despite variations in image quality.
Data Source
AI summary
An inspection method includes receiving a plurality of training images and an image of a target object obtained from inspection of the target object. The method further includes generating, by one or more training codes, a plurality of inference codes. The one or more training codes are configured to receive the plurality of training images as input and output the plurality of inference codes. The one or more training codes and the plurality of inference codes includes computer executable instructions. The method further includes selecting one or more inference codes from the plurality inference codes based on a user input and/or one or more characteristics of at least a portion of the received plurality of training images. The method also includes inspecting the received image using the one or more inference codes of the plurality of inference codes.