Battery Welding Image Detection for Automated Defect Inspection
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Solution Overview
Problem
Current battery welding defect detection methods rely on manual inspection, which is time-consuming, inaccurate, and costly, failing to ensure consistency and reliability due to human fatigue and limited visual capabilities.
Innovation Solution
A battery welding defect detection system utilizing an acquisition processing module for image preprocessing, a defect statistic module for candidate frame classification, and a detection analysis module for automated defect recognition using a target detection model, enhancing image quality and applying deep learning techniques to improve efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection with microscope is used, then detection accuracy can be maintained at human level, but detection efficiency is severely restricted and production cost increases
Solution Approach 1:
The patent replaces the manual mechanical inspection system with an automated optical detection system. The system uses image acquisition devices to capture welding images and employs image processing algorithms to automatically analyze defects, substituting human visual inspection with automated computational methods. This resolves the contradiction by dramatically improving detection efficiency while maintaining high accuracy through algorithmic analysis.
Solution Approach 2:
The detection system performs self-analysis through automated image processing and defect recognition algorithms. The system independently processes welding images, identifies defects, and generates detection results without requiring continuous human intervention. This automation enables high-speed detection while maintaining consistent accuracy, resolving the efficiency-automation contradiction.
2Measurement precision
If manual inspection is used, then flexibility in handling various welding defects is maintained, but detection accuracy deteriorates due to inspector fatigue
Solution Approach 1:
The patent replaces manual inspection with automated image processing systems that do not experience fatigue. The system continuously processes welding images with consistent accuracy, eliminating the degradation of detection performance over time that occurs with human inspectors. This resolves the contradiction between maintaining high accuracy and reducing inspection time.
Solution Approach 2:
The system performs preliminary image processing including denoising, enhancement, and standardization before defect detection. This preprocessing prepares the images optimally for analysis, ensuring high detection accuracy from the start and eliminating the need for repeated inspections, thereby reducing total inspection time while maintaining precision.
3Productivity
If automated detection system is implemented, then detection efficiency is improved, but system complexity increases
Solution Approach 1:
The patent divides the detection system into distinct functional modules: image acquisition, preprocessing (denoising, enhancement, standardization), defect detection, and result analysis. This modular segmentation manages system complexity by organizing functions into separate, manageable components while maintaining high overall detection efficiency through coordinated operation of these modules.
Solution Approach 2:
The detection system is designed with multi-functional capabilities that handle various welding defect types through a unified platform. The same core system can detect different defect types (cracks, porosity, incomplete welding) by processing images through standardized pipelines, reducing the need for multiple specialized systems and managing complexity through universal design.
4Measurement precision
If deep learning models are used for defect detection, then detection accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary image preprocessing including denoising, enhancement, and standardization before feeding images to deep learning models. This preprocessing improves image quality and reduces noise, enabling the use of more efficient detection algorithms that require fewer computational resources while maintaining high accuracy. The preprocessing step prepares data optimally for subsequent analysis.
Solution Approach 2:
The detection process is segmented into multiple stages: preprocessing, feature extraction, and final classification. This segmentation allows computationally intensive deep learning operations to be applied only where necessary, rather than processing entire images through heavy models. The segmented approach reduces overall computational energy consumption while maintaining high detection accuracy through focused application of advanced algorithms.
Data Source
AI summary
The present disclosure provides a battery welding defect detection system, method, device, and storage medium, wherein the system includes: the acquisition processing module, configured to acquire the welding image of the battery welding object and pre-process the welding image to obtain the processed welding image; the defect statistic module, configured to classify and count the processed welding image based on the target detection model to obtain the plurality of battery candidate frames; and the detection analysis module, configured to obtain the corresponding battery detection result according to the preset threshold and the battery candidate frames by the target detection model. The present disclosure performs the defect defection on the processed battery welding image based on the target detection model, thereby significantly improving the detection efficiency and the accuracy of the results, and at the same time effectively reducing the production cost.