Efficient convolutional neural networks on Raspberry Pi for image classification

With the good performance of deep learning in the field of computer vision (CV), the convolutional neural network (CNN) architectures have become main backbones of image recognition tasks. With the widespread use of mobile devices, neural network models based on platforms with low computing power ar...

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Vydané v:Journal of real-time image processing Ročník 20; číslo 2; s. 21
Hlavní autori: Ju, Rui-Yang, Lin, Ting-Yu, Jian, Jia-Hao, Chiang, Jen-Shiun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Artificial neural networks
Cellular telephones
Computation
Computer Graphics
Computer Science
Computer vision
Data transmission
Deep learning
Electronic devices
Embedded systems
Image classification
Image Processing and Computer Vision
Inference
Machine learning
Monitoring systems
Multimedia Information Systems
Neural networks
Original Research Paper
Pattern Recognition
Semantics
Signal,Image and Speech Processing
Unmanned aerial vehicles
Model acceleration
Raspberry Pi
Model compression
Convolutional neural network
Edge computing platform
Image classification
ISSN:1861-8200, 1861-8219
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Popis
Shrnutí:With the good performance of deep learning in the field of computer vision (CV), the convolutional neural network (CNN) architectures have become main backbones of image recognition tasks. With the widespread use of mobile devices, neural network models based on platforms with low computing power are gradually being paid attention. However, due to the limitation of computing power, deep learning algorithms are usually not available on mobile devices. This paper proposes a lightweight convolutional neural network TripleNet, which can operate easily on Raspberry Pi. Adopted from the concept of block connections in ThreshNet, the newly proposed network model compresses and accelerates the network model, reduces the amount of parameters of the network, and shortens the inference time of each image while ensuring the accuracy. Our proposed TripleNet and other State-of-the-Art (SOTA) neural networks perform image classification experiments with the CIFAR-10 and SVHN datasets on Raspberry Pi. The experimental results show that, compared with GhostNet, MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time of TripleNet per image is shortened by 15%, 16%, 17%, 24%, and 30%, respectively. The detail codes of this work are available at https://github.com/RuiyangJu/TripleNet .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-023-01271-1