@Article{cmes.2023.026910,
AUTHOR = {Huabin Wang, Ruichao Mo, Yuping Chen, Weiwei Lin, Minxian Xu, Bo Liu},
TITLE = {Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {2},
PAGES = {2025--2047},
URL = {http://www.techscience.com/CMES/v137n2/53352},
ISSN = {1526-1506},
ABSTRACT = {Nowadays, the rapid development of edge computing has driven an increasing number of deep learning applications
deployed at the edge of the network, such as pedestrian and vehicle detection, to provide efficient intelligent services
to mobile users. However, as the accuracy requirements continue to increase, the components of deep learning
models for pedestrian and vehicle detection, such as YOLOv4, become more sophisticated and the computing
resources required for model training are increasing dramatically, which in turn leads to significant challenges
in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance. For addressing this challenge, a cloud-edge collaboration-based pedestrian and vehicle detection framework
is proposed in this paper, which enables sufficient training of models by utilizing the abundant computing resources
in the cloud, and then deploying the well-trained models on edge devices, thus reducing the computing resource
requirements for model training on edge devices. Furthermore, to reduce the size of the model deployed on edge
devices, an automatic pruning method combines the convolution layer and BN layer is proposed to compress the
pedestrian and vehicle detection model size. Experimental results show that the framework proposed in this paper
is able to deploy the pruned model on a real edge device, Jetson TX2, with 6.72 times higher FPS. Meanwhile, the
channel pruning reduces the volume and the number of parameters to 96.77% for the model, and the computing
amount is reduced to 81.37%.},
DOI = {10.32604/cmes.2023.026910}
}