Visual detection method and device for lithium battery pole piece

By adopting multi-angle image acquisition, deep learning model and adaptive threshold strategy in lithium battery electrode detection, the problems of low efficiency and low accuracy of traditional detection methods are solved, and high-precision detection and rapid detection speed are achieved in different environments.

CN119643559APending Publication Date: 2025-03-18HANGZHOU ZHUOXI INST OF BRAIN & INTELLIGENCE
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Patent Information

Application Number
CN202411727676.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

Traditional lithium battery electrode detection methods are inefficient and costly, and are susceptible to human factors and environmental changes, resulting in low detection accuracy and difficult to meet the needs of large-scale production.

Method used

Industrial cameras are used to acquire multi-angle images, combine temperature and humidity sensors to monitor environmental parameters in real time, feature extraction and defect identification are performed through deep learning models, detection results are optimized using adaptive threshold strategies, and detection models are optimized through lightweight algorithms to improve speed.

Benefits of technology

Maintain high-precision detection in different environments, reduce the impact of environmental factors on the detection results, significantly improve the detection speed and efficiency, and meet the needs of large-scale production.

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Abstract

The invention provides a lithium battery pole piece visual detection method and device, and the method comprises the steps: employing an industrial camera to collect a multi-angle image of a lithium battery pole piece, and employing a temperature and humidity sensor to monitor environment parameters in real time; the multi-angle image is preprocessed; performing feature extraction on the preprocessed multi-angle image by adopting a deep learning model to obtain multi-angle features; after defect identification is conducted on the multi-angle features through a trained neural network model, a defect identification result is obtained, and the trained neural network model adopts a self-adaptive threshold strategy considering environment parameters. According to the technical scheme provided by the invention, the visual detection precision of the lithium battery pole piece can be improved by considering environmental factors and adopting the adaptive threshold strategy.
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