Lightweight Deep Learning Based Intelligent Edge Surveillance Techniques
Open accessPublished version of a preprint
Abstract
Decentralized edge computing techniques have been attracted strongly attentions in many applications of intelligent internet of things (IIoT). Among these applications, intelligent edge surveillance (LEDS) techniques play a very important role to recognize object feature information automatically from surveillance video by virtue of edge computing together with image processing and computer vision. Traditional centralized surveillance techniques recognize objects at the cost of high latency, high cost and also require high occupied storage. In this paper, we propose a deep learning-based LEDS technique for a specific IIoT application. First, we introduce depthwise separable convolutional to build a lightweight neural network to reduce its computational cost. Second, we combine edge computing with cloud computing to reduce network traffic. Third, we apply the proposed LEDS technique into the practical construction site for the validation of a specific IIoT application. The detection speed of our proposed lightweight neural network reaches 16 frames per second in edge devices. After cloud server fine detection, the precision of the detection reaches 89\%. In addition, the operating cost at the edge device is only one-tenth of that of the centralized server.
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The iterative process of backpropagation allows the DNN to gradually refine its predictions and improve its overall performance.
“This iterative process allows the DNN to gradually refine its predictions and improve its overall performance [49,50].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [49]
Algorithm 2 says the network weights are trained and the loss is minimized, and the Experimental results section reports convergence over hundreds of epochs. These passages support iterative training, but our copy does not identify backpropagation as the mechanism or show that predictions and overall performance improve through that process; it cautions that accuracy cannot be compared directly from loss values. No page numbers are available in our copy. (LLM, conf=0.79)
Experimental — accuracy not yet measuredUsing a lightweight DNN with edge computing can reduce the computational cost of object detection.
“Using the lightweight DNN with edge computing can reduce the computational cost of object detection [49].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [49]
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A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134 · see citing works
“Using the lightweight DNN with edge computing can reduce the computational cost of object detection [49].”
Method
- Authors
- Zhao, Y., Yin, Y., & Gui, G.
- Affiliations
- Nanjing University of Posts and Telecommunications (CN)
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2020-12
- Volume
- 6
- Issue
- 4
- Pages
- 1146-1154
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W3006898026
- Cited by
- 76
- References
- 46
- Citations by year
- 2026: 3 · 2025: 17 · 2024: 13 · 2023: 12 · 2022: 12 · 2021: 17 · 2020: 2
- FWCI
- 5.05
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Project Funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China (TC190A3WZ-2)
- Six Top Talents Program of Jiangsu (XYDXX-010)
- 1311 Talent Plan of Nanjing University of Posts and Telecommunications
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- Internet of Things, edge computing, lightweight neural networks, depthwise separable convolution, object detection, surveillance video, cloud-edge collaboration, construction site monitoring, computational complexity reduction
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