The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
An Energy-efficient and Accurate Object Detection Design for Mobile Applications
Abstract
Deep Convolutional Neural Networks (DCNNs) are imperative to state-of-the-art computer vision algorithms. In spite of the attractive qualities of DCNN s, they have been excessively expensive to be applied on large scale high-resolution images and video sequences. In order to implement DCNN models on edge platforms, we tend to optimize the DCNN model by considering energy efficiency and detection accuracy simultaneously. In this paper, we analyze the energy consumption, detection accuracy, and execution speed of our model and those of the state-of-the-art models based on a mobile platform called Jetson Nano. We adopt the performance index from Low Power Computer Vision (LPCV) challenge which considers power, mAP and FPS at the same time to evaluate these models in an overall point of view. On Jetson Nano, the presented system boosted with the GoP-mode technique can achieve an execution speed of near 20 frames per second, and high mean average precision of 59.9% under MS COCO test sets. Compared with the state-of-the-art models, e.g., YOLOv5, the LPCV score improves as high as 76.33%. If the GoP-mode acceleration is included, the LPCV score of Agilev4 reaches even 90.6 times of that ofYOLOv5.
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“These algorithms are computationally less complex and suitable for a dynamically changing environment [67].”
From A Survey of Video Surveillance Systems in Smart City (2023) · cites this paper as [67]
Citations
Cites
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
Khanam, Z., Achari, V. P. S., Boukhennoufa, I., et al. (2024) · Durham Research Online (Durham University) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,048
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
An Analysis of Deep Neural Network Models for Practical Applications
Canziani, A., Paszke, A., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1605.07678 · Cited by 969
HarDNet: A Low Memory Traffic Network
Chao, P., Kao, C.-Y., Ruan, Y.-S., Huang, C., & Lin, Y.-L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00365 · Cited by 366
Evaluating the Energy Efficiency of Deep Convolutional Neural Networks on CPUs and GPUs
Li, D., Chen, X., Becchi, M., & Zong, Z. (2016) · 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) · doi:10.1109/bdcloud-socialcom-sustaincom.2016.76 · Cited by 236
The 2020 Low-Power Computer Vision Challenge
Hu, X., Chang, M., Chen, Y., Sridhar, R., Hu, Z., Xue, Y., Wu, Z., Pi, P., Shen, J., Tan, J., et al. (2021) · IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) · doi:10.1109/aicas51828.2021.9458522 · Cited by 3
Design of Agilev4 and Its Real-time Implementation on an Economic Mobile Platform
Chen, K.-H., & Su, C.-W. (2022) · International Conference on Consumer Electronics · doi:10.1109/icce53296.2022.9730566 · Cited by 2
Self-citation
Research works printed but not linked: 2
- 0, LOW-POWER COMPUTER VISION CHALLENGE 2021, LPCV
- 0, JTOP, Nvidia
Cited by
Sorted by citations
A Survey of Video Surveillance Systems in Smart City
Myagmar-Ochir, Y., & Kim, W. (2023) · Electronics · doi:10.3390/electronics12173567 · Cited by 134
Metadata
- Authors
- Chen, K.-H., Wang, J.-H., & Su, C.-W.
- Affiliations
- Feng Chia University (TW)
- PH.D. program of Electronical and Communications Engineering,Department of Electronic Engineer
- ORCID
- Chen, Kuan-Hung
- Venue
- 2022 IEEE International Conference on Consumer Electronics - Taiwan
- Publisher
- IEEE
- Published
- 2022-07-06
- Pages
- 65-66
- OpenAlex
- W4294069476
- Cited by
- 1
- References
- 8
- Citations by year
- 2023: 1
- FWCI
- 0.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) (publishedVersion)
- Funders
- Ministry of Science and Technology (110-2218-E-035-006)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
- Keywords
- object detection, energy efficiency, Jetson Nano, edge computing, model compression, YOLOv5, mean average precision, inference speed, mobile vision
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- Chen, K.-H., Wang, J.-H., & Su, C.-W. (2022). An Energy-efficient and Accurate Object Detection Design for Mobile Applications. 2022 IEEE International Conference on Consumer Electronics - Taiwan. https://doi.org/10.1109/icce-taiwan55306.2022.9869164
