Al-Rafidain Engineering Journal (AREJ)

Enhancing Drivers’ Attention By A Smart Binary Matching Machine to Avoid Accidents

Document Type : Research Paper

Author

Computer Engineering Technology Department, Engineering Technical College\Mosul, Northern Technical University, Mosul, Iraq

Abstract

Stress and sudden difficult situations have raised the risks of accidents down the roads. The drivers’ attention might be distracted out in seconds under unexpected circumstances, which could take place due to bad weather, vision problems, fatigue for long driving hours, damaged or broken Traffic light , and even children's noise inside the car.  In this paper, I proposed to develop a special colourful Deep Back Propagation Neural Network to enhance drivers’ attention by observing different traffic light cases using a suggested smart binary matching machine system in Python. The smart machine system will analyse and identify the real Traffic light  from art signs, broken or damaged ones; in addition to pedestrian signs based on a Database symbols for each case, which have taken the basic Traffic light  and signs, and developed them to damaged cases or unreal one, before making the right decision by the learned network, then send an enhanced feedback signal to the driver. The algorithm consisted of accurate image processing steps, with two long stages of full contents features extraction vectors to be handled by Red-Yellow-Green Shallow and  Deep Back Propagation Neural Networks (SBPNN) and (DBPNN) for each complex case. As a result, the algorithm rated a high accuracy  of  100%, which is the most important factor to maintain safety, recoding the true label output as 1-value, with a predicated tested ouput 1.0-value. The suggested system does not replace the driver's one decision, yet it is an enhancing backup classification and recognition system before things move out of control. The feedback signal calculated based on reducing costs for 2500 iterations with The leas minimum value 000012,and can be developed as a voice signal warning Message, to increase the awareness of the drivers, besides the warning text on the screen.  

Keywords

Main Subjects

References
  1. C. Possatti et al., “Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars,” 2019 International Joint Conference on Neural Networks (IJCNN), 2019, doi:https://doi.org/10.1109/IJCNN.2019.8851927.
  2. [1] Y. Zhu and W. Q. Yan, “Traffic sign recognition based on Deep Learning,” Multimedia Tools and Applications, vol. 81, no. 13, pp. 17779–17791, 2022. doi:10.1007/s11042-022-12163-0
  3. Y. Taha, “Evaluation of the Acceptance of the Hot Mix Asphalt Paving Mixture Using Backpropagation Artificial Neural Network,” AL-Rafdain Engineering Journal (AREJ), vol. 19, no. 2, pp. 40–54, Apr. 2011, doi: https://doi.org/10.33899/rengj.2011.27341.
  4. A. Weber et al., “DeepTLR: A single deep convolutional network for detection and classification of Traffic light ,” Jun. 2016, doi: https://doi.org/10.1109/ivs.2016.7535408.
  5. Sahar Qaddoori et al., “An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles,” vol. 28, no. 1, pp. 329–340, Mar. 2023, doi:https://doi.org/10.33899/rengj.2022.134932.1191.
  6. Gu et al., “A Novel Lightweight Real-Time Traffic Sign Detection Integration Framework Based on YOLOv4,” Entropy, vol. 24, no. 4, p. 487, Mar. 2022, doi: https://doi.org/10.3390/e24040487.
  7. Navarro-Espinoza  et al., “Traffic Flow Prediction for Smart Traffic light Using Machine Learning Algorithms,” Technologies, vol. 10, no. 1, p. 5, Jan. 2022, doi:https://doi.org/10.3390/technologies10010005.
  8. Wang et al., “Traffic light Detection and Recognition Method Based on the Improved YOLOv4 Algorithm,” Sensors, vol. 22, no. 1, p. 200, Dec. 2021, doi: https://doi.org/10.3390/s22010200.
  9. Li et al., “An improved Traffic light recognition algorithm for autonomous driving in complex scenarios,”  vol. 17, no. 5, p. 155014772110183-155014772110183, May 2021, doi: https://doi.org/10.1177/15501477211018374.
  10. T. John et al., “Traffic light recognition in varying illumination using deep learning and saliency map,” Oct. 2014, doi: https://doi.org/10.1109/itsc.2014.6958056.
  11. F. Berriel et al., “Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification,” IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 9, pp. 1513–1517, Sep. 2017, doi: https://doi.org/10.1109/lgrs.2017.2719863.
  12. Usha et al.,“Traffic Sign Classification Using Deep Learning,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 9, pp. 250–253, Apr. 2021, doi: https://doi.org/10.17762/turcomat.v12i9.3007
  13. Bichkar et al., “Traffic Sign Classification and Detection of Indian Traffic Signs using Deep Learning,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 215–219, May 2021, doi: https://doi.org/10.32628/cseit217325.
  14. Farag, “Traffic signs classification by deep learning for advanced driving assistance systems,” Intelligent Decision Technologies, vol. 13, no. 3, pp. 305–314, Sep. 2019, doi: https://doi.org/10.3233/idt-180064.
  15. Á. Arcos-García et al.,“Evaluation of deep neural networks for traffic sign detection systems,” Neurocomputing, vol. 316, pp. 332–344, Nov. 2018, doi: https://doi.org/10.1016/j.neucom.2018.08.009
  16. Fadhil et al.,“Real-Time Signature Recognition Using Neural Network,” Al-Rafidain Engineering Journal (AREJ), vol. 26, no. 1, pp. 159–165, Jan. 2021, doi:https://doi.org/10.33899/rengj.2021.129871.1088
  17. Zhang et al., “Lightweight deep network for traffic sign classification,” Annals of Telecommunications, vol. 75, no. 7–8, pp. 369–379, Jul. 2019, doi: https://doi.org/10.1007/s12243-019-00731-9
  18. Wong et al.,“MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-Time Embedded Traffic Sign Classification,” IEEE Access, vol. 6, pp. 59803–59810, 2018, doi: https://doi.org/10.1109/access.2018.2873948
  19. M. Elhawary et al.,“Investigation on the Effect of the Feature Extraction Backbone for Small Object Segmentation using Fully Convolutional Neural Network in Traffic Signs Application,” IOP Conference Series: Materials Science and Engineering, vol. 1051, no. 1, p. 012006, Feb. 2021,https://doi.org/10.1088/1757- 899x/1051/1/012006.
  20. Zhong et al.,“Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 2, pp. 847–858, Feb. 2018, doi: https://doi.org/10.1109/tgrs.2017.2755542.
  21. Liu et al.,“TSingNet: Scale-aware and context-rich feature learning for traffic sign detection and recognition in the wild,” Neurocomputing, vol. 447, pp. 10–22, Aug. 2021, doi: https://doi.org/10.1016/j.neucom.2021.03.049.
  22. Farag, “Recognition of traffic signs by convolutional neural nets for self-driving vehicles,” International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 22, no. 3, pp. 205–214, Nov. 2018, doi: https://doi.org/10.3233/kes-180385.
  23. Credi, “Traffic sign classification with deep convolutional neural networks Master’s thesis in Complex Adaptive Systems.”, Dept. of Applied Mechanics, Chalmers University of Technology, Gothenburg,Sweden. https://odr.chalmers.se/server/api/core/bitstreams/fdef1142-92cb-4f8c-9c8a-f17f72260c00/content
  24. Á. Arcos-García et al.,“Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods,” Neural Networks, vol. 99, pp. 158–165, Mar. 2018, doi: https://doi.org/10.1016/j.neunet.2018.01.005.
  25. [1] M. Lin, Q. Chen, and S. Yan, “Network in Network,” arXiv.org, https://arxiv.org/abs/1312.4400 (accessed Aug. 13, 2023).
  26. Shao et al.,“Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs,” Sensors, vol. 18, no. 10, p. 3192, Sep. 2018, doi: https://doi.org/10.3390/s18103192.
  27. Kondamari et al.,“A Deep Learning Application for Traffic Sign Classification,” Thesis, Blekinge Institute of Technology, 371 79 Karlskrona,Sweden, 2021.
  28. Vijayakumaran, “Recognition of Traffic Signs using Deep Learning,” Oct. 2020. Available: https://www.researchgate.net/profile/Tharmi-Vijayakumaran
  29. H. Shu et al.,“Network Traffic Classification Based on Deep Learning,” Journal of Physics: Conference Series, vol. 1087, p. 062021, Sep. 2018, https://doi.org/10.1088/1742- 596/1087/6/062021.
  30. “The Power of Deep Learning Models: Applications,” International Journal of Recent Technology and Engineering, vol. 8, no. 2S11, pp. 3700–3705, Nov. 2019, doi:https://doi.org/10.35940/ijrte.b1468.0982s1119
  31. Mathew et al., “Deep Learning Techniques: An Overview, ” Advances in Intelligent Systems and Computing, pp. 599–608, May 2020, doi:https://doi.org/10.1007/978-981-15-3383-9_54.
  32. B. Wali et al., “Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges,” Sensors, vol. 19, no. 9, p. 2093, May 2019, https://doi.org/10.3390/s19092093.
  33. A. Shanthi, “Traffic Sign Classification and Detection Using Deep Learning,” Apr. 2020. https://www.ijirt.org/master/publishedpaper/IJIRT149112_PAPER.pdf
  34. Andrew Ng,” Deep Learning Specialization DeepLearning.AI,” 2017.AI: Start or Advance Your Career in AI.
  35. [1] D. Edwards and Polly, “June 22, 2020,” Robotics & Automation News, https://roboticsandautomationnews.com/2020/06/22/.
  36. Soleymanpour et al., “Traffic Classification using Deep Learning,” Ayandegan Institute of Higher Education, Mar. 2020. doi:https://doi: 10.13140/RG.2.2.33375.82087
Al-Rafidain Engineering Journal (AREJ)
Volume 28, Issue 2
September 2023
Page 261-283
Files
Share
Export Citation
Statistics