The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
FPGA based Deep Learning Models for Object Detection and Recognition Comparison of Object Detection Comparison of object detection models using FPGA
Abstract
Real-time object detection and recognition finds extensive applications in diverse fields such as medical applications, security surveillance, and autonomous vehicles. There are many machines and deep learning techniques that are employed for object detection and recognition. The emergence of a convolutional neural network (CNN) has provided a major breakthrough for object detection and recognition. This work also includes the hardware implementation of the same with the help of Xilinx PYNQ Z2 and Intel Movidius Neural Compute Stick (NCS) which are proved to increase the performance of the system proposed. The results are compared based on three deep learning methods: Single Shot Detector (SSD), Faster Region CNN (FRCNN), You Only Look Once (YOLO). The parameters that are considered are frames per second, probability of detection, and time for computation. The results obtained are performing well compared to existing models.
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“FRCNN is the most accurate, with 100% for probability of detection [61].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [61]
In the citing paper
“In term of frames per second (FPS) of the object detection model, SSD is the highest, with 3.98 FPS, while YOLO is the fastest for the computational time, with 12.68 ms.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [61]
In the citing paper
“These CNN models require high processing times, where Xilinx PYNQ Z2 board and Intel Movidius Neural Compute Stick (NCS), a portable and low-powered device together play a major role to speed up the process.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [61]
In the citing paper
“In term of frames per second (FPS) of the object detection model, SSD is the highest, with 3.98 FPS, while YOLO is the fastest for the computational time, with 12.68 ms.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [61]
Citations
Cites
ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Advanced Deep-Learning Techniques for Salient and Category-Specific Object Detection: A Survey
Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2749125 · Cited by 625
Embedded Deep Learning for Vehicular Edge Computing
Hochstetler, J., Padidela, R., Chen, Q., Yang, Q. X., & Fu, S. (2018) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00038 · Cited by 74
A New Deep Learning Application Based on Movidius NCS for Embedded Object Detection and Recognition
Othman, N. A., & Aydın, İ. (2018) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit.2018.8567306 · Cited by 45
An Accelerated Prototype with Movidius Neural Compute Stick for Real-Time Object Detection
Kaarmukilan, S. P., Hazarika, A., Amal Thomas, K., Poddar, S., & Rahaman, H. (2020) · International Symposium on Devices, Circuits and Systems (ISDCS) · doi:10.1109/isdcs49393.2020.9262996 · Cited by 14
Self-citationLow-Cost IoT Surveillance System Using Hardware-Acceleration and Convolutional Neural Networks
Lage, E. D. S., Santos, R. L., Sandro, M. T., et al. (2019) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2019.8767325 · Cited by 11
Person Recognition for Access Logging
Boka, A., & Morris, B. (2019) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2019.8666483 · Cited by 9
Hand Gesture Recognition Using Movidius Neural Compute Stick
Hamid, M. F. A., & Zaman, F. H. K. (2019) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icsengt.2019.8906357 · Cited by 6
Research works printed but not linked: 3
- weng, 2017, Object detection for dummies part 3 R-cnn family
- 2020, Intel movidius neural compute stick
- crews, 2017, A developer’s introduction to Movidius neural compute stick
Cited by
Sorted by citations
Embedded Intelligence on FPGA: Survey, Applications and Challenges
Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105
“Furthermore, there are other research works [61] to review about object detection in embedded intelligence.”
Background
Metadata
- Authors
- Kaarmukilan, S. P., Poddar, S., & Thomas K., A.
- Affiliations
- Indian Institute of Information Technology Guwahati (IN)
- Indian Institute of Information Technology, Guwahati,Dept. of Electronics and Communication Engineering,Guwahati,Assam,India
- ORCID
- Poddar, Soumyajit
- Venue
- 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 471-474
- OpenAlex
- W3018620108
- Cited by
- 29
- References
- 14
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 11 · 2022: 2 · 2021: 8 · 2020: 1
- FWCI
- 1.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computing Methodologies and Communication (ICCMC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Embedded Systems and FPGA Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Keywords
- FPGA, hardware acceleration, object detection, object recognition, convolutional neural networks, SSD, Faster R-CNN, YOLO, real-time inference, performance comparison
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