Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN:
Online ISSN:

  About DSPAI
  DLINE Portal Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Paper Submission
Contact us
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy

  For Authors
  Guidelines for Contributors
Online Submission
Statement of Ethics and Responsibilities
Review Policies
Transfer of Copyright
Archiving Policy
Call for Papers
Author Rights
Journal of Digital Information Management (JDIM)
International Journal of Computational Linguistics Research (IJCL)
International Journal of Web Application (IJWA)


Digital Signal Processing and Artificial Intelligence for Automatic Learning


A Survey on Deep Learning Models Based Road Object Detection Inference
Omar BOUAZIZI ENSA, Abdelmalek Essaadi, Aimad EL MOURABIT ENSA
University Data Engineering & Systems TEAM Tangier Morocco., Abdelmalek Essaadi University Data Engineering & Systems TEAM Tangier Morocco
Abstract: In this paper, we present a comparative study of the object detection accuracy and speed of various state-of-the art models for the road scene context. Commensurate with the model training method, the algorithms can be divided into two types: one-stage models and two-stage models. We focused on the road context in order to detect all occurrences of objects in the road such as, car, person, traffic signs, etc. Accordingly, we find that one-stage detectors are stronger in terms of prediction speed, while two-stage models are stronger in terms of accuracy. To train deep neural networks with platform GPU type on a large amount of image data that required time, Because the computational cost of computer vision is very high, so we are focused to transfer learning technique, where a model trained on one task is reused on another related task, gives better results.
Keywords: Object Detection, Deep Learning, CNN, Transfer Learning, GPU A Survey on Deep Learning Models Based Road Object Detection Inference
Full_Text   PDF 768 KB   Download:   69  times

[1] (PDF) Review of advanced driver assistance systems (ADAS). https://www.researchgate.net/publication/ 321364551_Review_of_advanced_driver_assistance_systems_ADAS (accessed Aug. 25, 2021).
[2] Prabhu. (2020). Understanding of Convolutional Neural Network (CNN) — Deep Learning, Medium, Nov. 21, 2019. https:// medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148 (accessed Oct. 10, 2020).
[3] Deng, J., Xuan, X., Wang, W., Li, Z., Yao, H., Wang, Z. (2020). A review of research on object detection based on deep learning, J. Phys. Conf. Ser., 1684, 012028, November 2020, doi: 10.1088/1742-6596/1684/1/012028.
[4] Mittal, U., Srivastava, S., Chawla, P. (2019). Review of different techniques for object detection using deep learning, In: Proceedings of the Third International Conference on Advanced Informatics for Computing Research, New York, NY, USA, June 2019, 1–8. doi: 10.1145/3339311.3339357.
[5] A comprehensive and systematic look up into deep learning based object detection techniques: A review - ScienceDirect. https://www.sciencedirect.com/science/article/abs/pii/S1574013720304019 (accessed Jun. 20, 2021).
[6] Murthy, C. B., Hashmi, M. F., Bokde, N. D., Geem, Z. W. (2020). Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review, Applied Sciences, 10 (9), 9, January 2020, doi: 10.3390/app10093280.
[7] Groener, A., Chern, G., Pritt, M. (2019). A Comparison of Deep Learning Object Detection Models for Satellite Imagery,” 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 1–10, October 2019, doi: 10.1109/AIPR47015.2019.9174593.
[8] Liu, W., et al. (2016). SSD: Single Shot MultiBox Detector, in Computer Vision – ECCV 2016, Cham, 2016, 21–37. doi: 10.1007/ 978-3-319-46448-0_2.
[9] Lin, T.-Y., et al. (2021). Microsoft COCO: Common Objects in Context,” arXiv:1405.0312 [cs], Feb. 2015, Accessed: Aug. 25, 2021. [Online]. Available: http://arxiv.org/abs/1405.0312
[10] Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., Zisserman, A. (2010). The Pascal Visual Object Classes (VOC) Challenge, Int J Comput Vis, 88 (2) 303–338, June, doi: 10.1007/s11263-009-0275-4.
[11] Cordts, M. et al. (2020). The Cityscapes Dataset for Semantic Urban Scene Understanding,” arXiv:1604.01685 [cs], Apr. 2016, Accessed: October 10. [Online]. Available: http://arxiv.org/abs/1604.01685
[12] BDD100K: A Large-scale Diverse Driving Video Database – The Berkeley Artificial Intelligence Research Blog. https:// bair.berkeley.edu/blog/2018/05/30/bdd/ (accessed Aug. 25, 2021).
[13] Kuznetsova, A., et al. (2020). The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale, Int J Comput Vis, 128 (7) 1956–1981, July 2020, doi: 10.1007/s11263-020-01316-z.
[14] Al-refai, G., Al-refai, M. (2020). Road Object Detection using Yolov3 and Kitti Dataset, IJACSA, 11 (8) 2020, doi: 10.14569/ IJACSA.2020.0110807.
[15] Pan, S. J., Yang, Q. (2009). A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng., 22 (10), 1345–1359, Oct. 2010, doi: 10.1109/TKDE. 2009.191.
[16] Athanasiadis, I., Mousouliotis, P., Petrou, L. (2020). A Framework of Transfer Learning in Object Detection for Embedded Systems, arXiv:1811.04863 [cs], Nov. 2018, Accessed: June 30, 2020. [Online]. Available: http://arxiv.org/abs/1811.04863
[17] Sanjay Kumar, K. K. R., Subramani, G., Thangavel, S. K., Parameswaran, L. (2021). A Mobile-Based Framework for Detecting Objects Using SSD-MobileNet in Indoor Environment, In Intelligence in Big Data Technologies—Beyond the Hype, Singapore, 2021, p 65–76. doi: 10.1007/978-981-15-5285-4_6.
[18] Geiger, A., Lenz, P., Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite, in 2012 IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2012, pp. 3354–3361. doi: 10.1109/CVPR.2012.6248074.
[19] Carte graphique NVIDIA Quadro RTX 8000, NVIDIA. https://www.nvidia.com/fr-fr/design-visualization/quadro/rtx-8000/ (accessed Aug. 25, 2021).
[20] TensorFlow Core. https://www.tensorflow.org/tutorials?hl=fr (accessed Oct. 10, 2020).

Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|


Copyright 2011 dline.info