基于深度学习的废旧动力蓄电池回收检测技术研究
Deep Learning-Based Recycling and Detection Technology for Used Power Batteries
于翔 1刘环宇 1郝皓1
作者信息
摘要
针对当前废旧动力蓄电池回收运输前对电池健康度采用人工检测存在的安全性低、精度较差、效率低下等问题,通过将机器视觉技术与废旧动力蓄电池回收检测应用相融合,提出了基于深度学习的废旧动力蓄电池回收检测技术.其利用深度学习技术自动判断电池安全状态,有效提高了电池回收检测流程的准确度.经现场应用后,该系统实现了电池状态的自动识别,实现了检测过程中的无人化、自动化,提高了电池回收企业的安全管理水平.
Abstract
Aiming at the safety,poor accuracy and low efficiency of manual inspection of battery health before recycling and transportation of used power batteries,a deep learning-based recycling inspection technology for used power batteries is proposed by integrating the machine vision technology with the recycling inspection application of used power batteries,and the deep learning technology is used to automatically determine the safety status of the battery,which effectively improves the accuracy of the battery recycling inspection process.Accuracy.After field application,the system achieves automatic identification of battery state,realises unmanned automation in the detection process,and improves the safety management level of battery recycling enterprises.
关键词
深度学习/动力蓄电池/电池回收/分类检测Key words
deep learning/power battery/battery recycling/classification detection引用本文复制引用
出版年
2024
NETL
NSTL
万方数据