Automated vehicles, advanced surveillance systems, AR, and
robots are just a few of the many new uses for real-time object
recognition. While deep learning models
are becoming increasingly complex and accurate, they might be challenging to
execute on edge devices with limited resources due to the computational demands.
By offloading computationally intensive processes to scalable cloud
infrastructure, cloud-enabled deep learning enables real-time processing
without sacrificing detection accuracy, offering an effective alternative. This study takes a close look at the current
setup of cloud-based object recognition methods that work in real time. When
considering latency, bandwidth, privacy, and processing costs, the pros and
cons of several architectural paradigms are evaluated, including hybrid
methodology, distributed inference, and edge-cloud cooperation. Additionally,
the developments of lightweight convolutional neural networks (CNNs),
single-shot detectors, and model compression techniques are examined, all of
which are aimed at real-time performance in cloud environments. Improving fault tolerance, optimizing data
transmission, safeguarding data security and privacy, and developing more
adaptive and efficient cloud resource management strategies for dynamic
real-time object detection contexts are all areas that could be further
explored in this review.
Author(s) Details
Abdul Razzak Khan
Qureshi
Department of Computer Science, Medicaps University, Indore, Madhya
Pradesh, India.
Ruby Bhatt
Department of Computer Science, Medicaps University, Indore, Madhya
Pradesh, India.
Govinda Patil
Department of Computer Science, Medicaps University, Indore, Madhya
Pradesh, India.
Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v6/5867
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