  With the good performance of deep learning algorithms in the field of
computer vision (CV), the convolutional neural network (CNN) architecture has
become a main backbone of the computer vision task. 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.
