Battery Cell Lug Image Segmentation for Folding Defect Detection
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Solution Overview
Problem
Existing methods for detecting lug defects in battery cells are not accurate and efficient, leading to quality issues such as overall folding, partial folding, and missing lugs, which can cause safety hazards like short circuits.
Innovation Solution
A lug defect detection method and system that uses machine vision inspection devices to collect and process images of the lug area, setting baselines and detecting target lug images in defined areas to determine if a cell is defective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If industrial vision combined with correction devices is used for quality detection, then the detection can be performed, but the detection results are not accurate enough to identify folding lugs and missing lugs
Solution Approach 1:
The detection area is segmented into multiple regions (first detection area, second detection area, third detection area) based on the baseline. Each region is independently analyzed for specific defect types, allowing precise identification of folding lugs, missing lugs, and other defects through regional consistency algorithms and template matching in each segment
Solution Approach 2:
The patent introduces a baseline dimension to divide the detection space into multiple areas. By adding this reference dimension and analyzing lug presence in different spatial regions relative to the baseline, the system achieves more accurate detection of lug defects compared to traditional single-area vision inspection
2Productivity
If traditional vision detection methods are used, then the detection process can be completed, but the detection speed and accuracy for defective lugs are insufficient
Solution Approach 1:
The system performs preliminary actions by pre-establishing the baseline, dividing detection areas beforehand, and preparing template images for comparison. This preliminary setup enables faster real-time detection during production without sacrificing accuracy, as the detection algorithm can directly compare captured images against pre-defined regional templates
Solution Approach 2:
The patent uses template copying and comparison methods where standard lug images are stored as templates. The detected lug images in each region are copied and compared against these templates to quickly identify deviations, enabling high-speed accurate detection suitable for production line speeds
Data Source
AI summary
The present disclosure provides a lug defect detection method and system. The detection method includes during a preparation process of a battery cell, collecting original image of a relevant area of lug; in a digitally processed original image, setting a baseline based on an edge position of the pole piece body, and from the baseline, setting a side close to the pole piece body as a first detection area, setting another side away from the pole piece body as a second detection area; and detecting a target lug image in the first detection region and the second detection region according to a preset sequence, and determining whether a currently detected cell is a defective cell.