Battery Cell Weld Region Segmentation for Defect Detection
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Solution Overview
Problem
Existing weld defect detection methods for battery cells suffer from high misjudgment rates during the detection of defects between the battery cell and the top cover, affecting production yield and safety.
Innovation Solution
A method involving region division of the weld into multiple detection regions, including upper top cover, side top cover, and center bead regions, with specific edge location determination and attribute information analysis to accurately identify defects, using structured image analysis and deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct defect location positioning is performed without region division, then detection speed is maintained, but misjudgment rate increases
Solution Approach 1:
The weld detection area is segmented into multiple distinct regions (upper top cover region, side top cover region, center bead region) based on the weld structure. Each region is independently analyzed with region-specific detection criteria, which reduces misjudgment rates by avoiding uniform detection across heterogeneous areas while maintaining a manageable detection process through systematic regional breakdown.
2Measurement precision
If uniform detection criteria are applied across the entire weld area, then detection process is simplified, but detection accuracy decreases due to high misjudgment rate
Solution Approach 1:
Different detection criteria and parameters are applied to different weld regions according to their specific characteristics. The upper top cover region, side top cover region, and center bead region each have customized detection thresholds and attribute requirements, enabling accurate defect identification in each zone while maintaining operational simplicity through automated region-specific rule application.
3Measurement precision
If detailed region division with multiple parameters is implemented, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The weld detection problem is divided into three independent regional detection tasks with distinct parameter sets. This segmentation reduces computational complexity compared to analyzing the entire weld area with a single complex model, as each region can be processed with optimized, simpler criteria tailored to its specific characteristics, thereby reducing overall computational power requirements while maintaining high accuracy.
Data Source
AI summary
The present application provides a welding defect detection method and apparatus, a storage medium, and a program product, and relates to the field of batteries. A weld region and non-weld regions are obtained by using a first image having structural information, a plurality of detection regions of a weld are obtained according to the weld region and the non-weld regions, and it is determined that the weld has a defect when preset attribute information of at least one detection region in the plurality of detection regions of the weld satisfies a preset condition.