Electrode Sheet Inspection Using Channel Attention for Misalignment Detection
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Solution Overview
Problem
Existing methods for detecting the misalignment between anode and cathode plates in lithium batteries are prone to interference and inaccuracies, leading to potential lithium precipitation and safety issues.
Innovation Solution
A battery cell electrode plate detection method and apparatus that performs feature extraction on images of the electrode plates using a channel attention mechanism, embedding position information to enhance feature extraction and improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional detection methods are used for electrode plate alignment, then the detection process is simple, but the detection accuracy is low and prone to interference
Solution Approach 1:
The patent applies preliminary action by performing feature extraction and channel attention processing before the actual detection. The method pre-processes the electrode plate images to extract effective features and embed position information, which prepares the data in advance to improve detection accuracy while managing complexity through structured preprocessing steps
Solution Approach 2:
The patent introduces an intermediary mechanism through the channel attention module that acts as a mediator between the raw image data and the final detection result. This module selectively enhances important feature channels and embeds position information, filtering out interference while maintaining detection accuracy without requiring complete system redesign
2Measurement precision
If the feature extraction process includes channel attention mechanism and position information embedding, then the detection accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies local quality by implementing channel attention mechanism that selectively processes different feature channels with different levels of attention. Instead of uniformly processing all features, the method identifies and enhances important local feature regions while reducing processing of less important areas, thereby improving detection accuracy while controlling computational power consumption
3Reliability
If the amount of misalignment is not properly detected, then the production process is fast, but lithium precipitation occurs leading to safety issues
Solution Approach 1:
The patent replaces traditional mechanical or manual inspection methods with an automated deep learning-based detection system. The method uses neural networks with channel attention mechanisms to automatically detect misalignment, ensuring high reliability for battery safety while maintaining production efficiency through automated processing that does not require manual intervention for each inspection
Data Source
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AI summary
This application relates to the field of battery technologies, and provides a battery cell electrode plate detection method and apparatus, and an electronic device. The method includes: performing feature extraction on a to-be-detected battery cell electrode plate image to obtain an electrode plate feature, where the electrode plate feature includes a channel attention feature embedded with position information; performing electrode plate detection based on the electrode plate feature to obtain an electrode plate detection result; and determining an amount of misalignment between an anode plate and a cathode plate based on position information of the anode plate and the cathode plate in the electrode plate detection result. According to the technical solution provided in this application, the amount of misalignment between the cathode plate and the anode plate can be detected.