Huabin Wang1, Ruichao Mo2, Yuping Chen3, Weiwei Lin2,4,*, Minxian Xu5, Bo Liu3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2025-2047, 2023, DOI:10.32604/cmes.2023.026910
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… More >
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