The week's pick
Reseach Article
Effectiveness of Deep Learning in Real Time Object Detection
by
Faysal Hossain,
Md. Raihan-Al-Masud,
M. Rubaiyat Hossain Mondal
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 41 |
| Year of Publication: 2020 |
| Authors: Faysal Hossain, Md. Raihan-Al-Masud, M. Rubaiyat Hossain Mondal |
10.5120/ijca2020920551
|
Faysal Hossain, Md. Raihan-Al-Masud, M. Rubaiyat Hossain Mondal . Effectiveness of Deep Learning in Real Time Object Detection. International Journal of Computer Applications. 176, 41 ( Jul 2020), 55-60. DOI=10.5120/ijca2020920551
@article{
10.5120/ijca2020920551,
author = {
Faysal Hossain,
Md. Raihan-Al-Masud,
M. Rubaiyat Hossain Mondal
},
title = { Effectiveness of Deep Learning in Real Time Object Detection },
journal = {
International Journal of Computer Applications
},
issue_date = { Jul 2020 },
volume = { 176 },
number = { 41 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = {
55-60
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume176/number41/31478-2020920551/
},
doi = { 10.5120/ijca2020920551 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:04.530305+05:30
%A Faysal Hossain
%A Md. Raihan-Al-Masud
%A M. Rubaiyat Hossain Mondal
%T Effectiveness of Deep Learning in Real Time Object Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 41
%P 55-60
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Deep learning based object detection has recently gained significant interest. This work focuses on real time object detection using two deep learning models named Faster Regional Convolution Neural Network (Faster-RCNN) and MobileNet Single Shot MultiBox Detector (MobileNet-SSD). An experiment is done using Python for programming, TensorFlow library for computing and OpenCV for computer vision. The Faster-RCNN and MobileNet-SSD models are trained using 400 images of four objects which are persons, watches, cell phones, and books. It is shown that for the images considered, Faster-RCNN can successfully detect these four objects with higher accuracy than MobileNet-SSD. Faster-RCNN also requires less time than MobilneNet-SSD for training the objects. However, Faster-RCNN model is slightly slower than MobileNet-SSD in real time object detection.
References
- Mathe, S., and Sminchisescu, C., "Actions in the eye: dynamic gaze datasets and learnt saliency models for visual recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 7, pp. 1408-1424, July 1 2015. doi: 10.1109/TPAMI.2014.2366154.
- Chellappa, R. et al., "Towards the design of an end-to-end automated system for image and video-based recognition," Information Theory and Applications Workshop, La Jolla, CA, 2016, pp. 1-7.
- Nikan, S. and Ahmadi, M., "Effectiveness of various classification techniques on human face recognition," 2014 International Conference on High Performance Computing & Simulation (HPCS), Bologna, 2014, pp. 651-655. doi: 10.1109/HPCSim.2014.6903749.
- Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S. and Ma, Y., "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, pp. 210-227, February 2009.
- Huang, G. B., Zhou, H., Ding, X. and Zhang, R., "Extreme learning machine for regression and multiclass classification," IEEE Trans. Syst., Man, Cybern., Syst., vol. 45, pp. 513-529, April 2012.
- Nikan, S. and Ahmadi, M., "Study of the Effectiveness of Various Feature Extractors for Human Face Recognition for Low Resolution Images," in: Proc. International Conf. on Artificial Intell. and Software Eng. (AISE14). Phuket, pp. 1-6, January 2014.
- Wu, J., Ma, L. and Hu, X., "Delving deeper into convolutional neural networks for camera relocalization," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 5644-5651. doi: 10.1109/ICRA.2017.7989663.
- N. Kumar and A. Sethi, "Fast Learning-Based Single Image Super-Resolution," in IEEE Transactions on Multimedia, vol. 18, no. 8, pp. 1504-1515, Aug. 2016. doi: 10.1109/TMM.2016.2571625.
- C. M. Bishop, “Pattern Recognition and Machine Learning (Information Science and Statistics)”. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.
- S. Ren, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, CoRRabs/1506.01497(2015). URL: http://arxiv.org/abs/1506.01497.
- N. Ketkar, “Deep Learning with Python: A Hands-on Introduction”, Bangalore, Karnataka, India, ISBN-13 (electronic): 978-1-4842-2766-4, DOI 10.1007/978-1-4842-2766-4.
- I. Goodellow, Y. Bengio, A. Courville, “Deep Learning (Adaptive Computation and Machine Learning)”, An MIT Press Book, URL: https://www.deeplearningbook.org/ Accessed: 2018-05-28.
- G. Ross, “Fast R-CNN”, Proceedings of the IEEE International Conference on Computer Vision. 2015, pp. 1440–1448.
- J. R. R. Uijlings, et al, "Selective search for object recognition", URL: http://disi.unitn.it/ uijlings/SelectiveSearch.html.
- W. Liu, C. Szegedy, SSD: Single Shot MultiBox Detector, In arXiv:1512.02325.
- R. Girshick, J. Donahue, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5), URL: http://arxiv.org: 1311.2524.
- J. Redmon, S. Divvala, Girshick, R., and Farhadi, A., "You only look once: unified, real-time object detection," IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016, pp. 779-788.
- Bharati S., Podder P., and Mondal, M. R. H., “Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review”, International Journal of Computer Information Systems and Industrial Management Applications, MIR Labs, USA, vol. 12 (2020), pp. 125-137, May 2020.
- Bharati S., Podder P., and Mondal, M. R. H., Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms. Presented at 2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Bangladesh.
- Masud, M. R. A., and Mondal, M. R. H., "Data-Driven Diagnosis of Spinal Abnormalities Using Feature Selection and Machine Learning Algorithms," in PLOS One, 15(2): e0228422, Feb 2020,https://doi.org/10.1371/journal.pone.0228422.
- Kabir, M. A., and Mondal, M. R. H., "Edge-Based and Prediction-Based Transformations for Lossless Image Compression", Journal of Imaging, vol. 4, no. 5, DOI: 10.3390/jimaging4050064, May 2018.
- Kabir, M. A., and Mondal, M. R. H., "Edge-based Transformation and Entropy Coding for Lossless Image Compression". International Conference on Electrical, Computer and Communication Engineering (ECCE 2017), Cox's Bazar, Bangladesh, Feb 2017.
- Khanam F., Nowrin I., and Mondal M. R. H., “Data Visualization and Analyzation of COVID-19”, Journal of Scientific Research and Reports, vol. 26, no. 3, pp. 42-52, Apr. 2020, https://doi.org/10.9734/jsrr/2020/v26i330234.
- Mondal, M. R. H., Bharati, S., Podder, P., Podder, P., “Data Analytics for Novel Coronavirus Disease”, Informatics in Medicine Unlocked, Elsevier, 2020, 100374, https://doi.org/10.1016/j.imu.2020.100374.
- Bharati S., Podder P., Mondal M.R.H., Hybrid deep learning for detecting lung diseases from X-ray images, Informatics in Medicine Unlocked, Elsevier, Volume 20, 2020, 100391, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2020.100391.
- Stanford Lecture: http://cs231n.github.io/. Accessed: 2018-05-28.
- LabelImage, https://github.com/tzutalin/labelImg. Accessed:2018-05-28.
Index Terms
Computer Science
Information Sciences
Keywords