机电工程技术2024，Vol.53Issue(11)：138-142,152,6.DOI:10.3969/j.issn.1009-9492.2024.11.030
基于深度学习的新能源电池溯源二维码机器视觉检测系统设计
Design of a Deep Learning-Based New Energy Battery Traceability QR Code Machine Vision Inspection System
摘要
Abstract
The traceability management in the production,utilization and recycling of new energy batteries should base on QR code information,which requires the precise and accurate labeling of traceability QR codes and alphanumeric identifiers on battery boxes in accordance with national standards.To guarantee the quality and correctness of these printed QR code images and alphanumeric characters,it is essential to conduct quality inspections prior to the assembly of new energy battery boxes.Consequently,a study and design of a traceability QR code machine vision inspection system are undertaken,leveraging the application of deep learning models.The analysis of detection requirements for new energy traceability QR codes is conducted,and the selection of hardware components such as cameras,lenses,and light sources for the machine vision inspection system,along with the design of the acquisition system,is performed.Deep learning techniques are employed to facilitate the classification,labeling,and model development of traceability QR codes.Image processing software is utilized to develop the QR code and character detection and verification algorithms,and the programs are encapsulated to finalize the design of the QR code detection user software.The designed new energy battery traceability QR code machine vision inspection system undergoes testing and validation.The results indicated that the system achieves a production efficiency of 15 EA/min,with a detection accuracy of 0.01 mm,and is capable of accurately identifying incorrect QR codes and characters.This system enables rapid and precise quality inspection and sorting of battery box QR codes,fulfilling the enterprise's requirements for automated and efficient detection,thereby enhancing production efficiency.关键词
新能源电池/溯源二维码/深度学习/机器视觉Key words
new energy battery/traceability QR code/deep learning/machine vision分类
信息技术与安全科学引用本文复制引用
龙淑嫔,廖书真,陈胜利,周旭华,陈宁..基于深度学习的新能源电池溯源二维码机器视觉检测系统设计[J].机电工程技术,2024,53(11):138-142,152,6.基金项目
2022年度广东省普通高校重点领域专项资金项目(2022ZDZX3080) （2022ZDZX3080）
2022年河源职业技术学院科技计划项目(2022KY-07) （2022KY-07）
