摘要
随着自动化生产线智能化需求的不断提升,基于深度学习的视觉识别技术成为克服传统局限性的关键。阐述了深度学习在自动化生产线视觉识别中的应用,通过图像预处理、特征提取、神经网络模型构建与训练等环节,显著提升了生产线的性能。通过案例分析验证了视觉识别技术的有效性,并对面临的挑战和未来发展趋势进行了探讨,为视觉识别技术的进一步应用提供了一定的理论指导和实践参考。
With the increasing demand for intelligence in automated production lines,deep learning-based visual recognition technology has become the key to overcoming traditional limitations.This paper elaborates on the application of deep learning in visual recognition of automated production lines,which significantly improves the performance of the production line through image preprocessing,feature extraction,neural network model construction and training.The effectiveness of visual recognition technology was verified through case analysis,and the challenges and future development trends faced were discussed,providing theoretical guidance and practical reference for the further application of visual recognition technology.
作者
杨心瀚
余睿
沙磊
高丽媛
姚明丙
YANG Xinhan;YU Rui;SHA Lei;GAO Liyuan;YAO Mingbing(Hongyun Honghe Tobacco(Group)Co.,Ltd.,Kunming,Yunnan 650032,China)
出处
《自动化应用》
2025年第12期1-3,共3页
Automation Application
基金
红云红河烟草(集团)有限责任公司专题科技项目“烟包外观质量与小盒商标含水率、定量间的量化关系研究”(HYHH2022CL02)。
关键词
深度学习
视觉识别
自动化生产线
神经网络
图像预处理
deep learning
visual recognition
automated production line
neural network
image preprocessing
