基于深度学习的卷积神经网络图像识别技术研究
Journal: Advances in Computer and Autonomous Intelligence Research DOI: 10.12238/acair.v2i2.7367
Abstract
本文针对基于深度学习卷积神经网络的图像识别技术进行了详细分析和深入研究。首先,梳理并介绍了图像识别技术的发展历程和卷积神经网络的基本原理。其次,重点分析了几种经典的卷积神经网络模型,如AlexNet、VGGNet、GoogLeNet和ResNet等,并对它们的网络结构、特点及优缺点进行了详细阐述。接着,探讨了卷积神经网络在图像识别领域的具体应用,包括图像分类、目标检测、语义分割、人脸识别、自然语言处理等方面,以及目前在这些应用中所面临的机遇与挑战。最后,展望了图像识别技术的发展趋势,提出了几点建议,如模型轻量化、Few-Shot Learning、多模态融合等,以期为相关研究提供有价值的参考。
Keywords
图像识别；深度学习；卷积神经网络；应用；发展趋势
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[1] 周榕,王博强,王锴.基于卷积神经网络的火焰图像识别技术的研究[J].热能动力工程,2024,39(02):184-193.
[2] 庄建,张晶,许钰雯.深度学习图像识别技术[M].机械工业出版社,2020,02.286.
[3] 杨子贤.基于深度学习卷积神经网络图像识别技术的研究与应用[J].中国设备工程,2018,(23):146-149.
[4] S.Hickson,N.Dufour,A. Sud,V.Kwatra and I.Essa. Eyemoti on: Classifying Facial Expressions in VR Using Eye-Tracking Cameras[C].2019 IEEE Winter Conference on Applications of Computer Vision (WACV),2019.
[5] Xiaohong L ,XiangfengL.Research on Image Recognition Method of Convolutional Neural Network with Improved Compu ter Technology[J].Journal of Physics: Conference Series,2021,1744(4):42023.
[2] 庄建,张晶,许钰雯.深度学习图像识别技术[M].机械工业出版社,2020,02.286.
[3] 杨子贤.基于深度学习卷积神经网络图像识别技术的研究与应用[J].中国设备工程,2018,(23):146-149.
[4] S.Hickson,N.Dufour,A. Sud,V.Kwatra and I.Essa. Eyemoti on: Classifying Facial Expressions in VR Using Eye-Tracking Cameras[C].2019 IEEE Winter Conference on Applications of Computer Vision (WACV),2019.
[5] Xiaohong L ,XiangfengL.Research on Image Recognition Method of Convolutional Neural Network with Improved Compu ter Technology[J].Journal of Physics: Conference Series,2021,1744(4):42023.
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