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Algorithm to Assist Visually Impaired Person for Object Detection in Real Time

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Emerging Electronics and Automation (E2A 2022)

Abstract

This paper has presented an object detection algorithm to assist people with vision issues. Blind people depend on others because it is very difficult for them to carry out daily tasks. People who are visually challenged frequently carry a white cane or guide dogs to help them see any impediments in their path. The proposed system can recognize objects in front of the blind person. For object detection and obstacle avoidance, the design includes a camera and an advanced image processing algorithm. This technology can be used in real time as it is so fast in detecting objects. This paper has successfully shown an algorithm for object detection of various objects which can be applied in the future work. There have been so many assisted devices available for the blind but challenge lies in how fast is the real-time object detection. The algorithm presented in this paper is faster than the algorithms that were presented earlier for object detection for assistive aids for blinds.

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References

  1. World Health Organization, G.S.: Blindness and vision impairment. https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment. Last accessed 27 July 2022

  2. Gupta B, Chaube A, Negi A, Goel U (2017) Study on object detection using open CV-Python. Int J Comput Appl 162:17–21

    Google Scholar 

  3. Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp 818–833

    Google Scholar 

  4. O’Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458

  5. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  6. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) sd: Single shot multibox detector. In: European conference on computer vision. Springer, pp 21–37

    Google Scholar 

  7. Krishna CV, Rohit HR (2018) A review of Artificial Intelligence methods for data science and data analytics: applications and research challenges. In: 2018 2nd international conference on 2018 2nd international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC). IEEE, pp 591–594

    Google Scholar 

  8. Python O-CI Evaluation of object tracking system using

    Google Scholar 

  9. GV B (2021) Object detection using OpenCV and deep learning. Int J Res Appl Sci Eng Technol 9:3920–3923. https://doi.org/10.22214/ijraset.2021.35880

  10. Kuruvilla C, Thomas S, Prasad P (2021) Real time indoor object detection aid for blind. IARJSET 8:617–623. https://doi.org/10.17148/iarjset.2021.86106

  11. Chandan G, Jain A, Jain H (2018) Real time object detection and tracking using deep learning and OpenCV. In: 2018 international conference on inventive research in computing applications (ICIRCA). IEEE, pp 1305–1308

    Google Scholar 

  12. Zhou Z, Lan X, Li S, Zhu C, Chang H (2019) Feature Pyramid SSD: outdoor object detection algorithm for blind people. In: 2019 IEEE 5th international conference on computer and communications (ICCC). IEEE, pp 650–654

    Google Scholar 

  13. Deshpande S, Shriram R (2016) Real time text detection and recognition on hand held objects to assist blind people. In: 2016 international conference on automatic control and dynamic optimization techniques (ICACDOT). IEEE, pp 1020–1024

    Google Scholar 

  14. Jogin M, Madhulika MS, Divya GD, Meghana RK, Apoorva S (2018) Feature extraction using convolution neural networks (CNN) and deep learning. In: 2018 3rd IEEE international conference on recent trends in electronics, information and communication technology (RTEICT). IEEE, pp 2319–2323

    Google Scholar 

  15. Zereen AN, Corraya S (2017) Detecting real time object along with the moving direction for visually impaired people. In: ICECTE 2016—2nd international conference on electrical, computer and telecommunication engineering. Inst Electr Electron Eng Inc. (2017). https://doi.org/10.1109/ICECTE.2016.7879628

  16. Roman Orac: What’s new in YOLOv4? https://towardsdatascience.com/whats-new-in-yolov4-323364bb3ad3. Last accessed 27 July 27

  17. Vaigai college of engineering, institute of electrical and electronics engineers: proceedings of the international conference on intelligent computing and control systems (ICICCS 2020): 13–15 May, 2020

    Google Scholar 

  18. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: Optimal speed and accuracy of object detection. ArXiv preprint arXiv:2004.10934 (2020)

  19. Páez-Montoro A, García-Valderas M, Olías-Ruíz E, López-Ongil C (2022) Solar energy harvesting to improve capabilities of wearable devices. Sensors 22:3950

    Article  Google Scholar 

  20. Siddique ARM, Mahmud S, van Heyst B (2017) A review of the state of the science on wearable thermoelectric power generators (TEGs) and their existing challenges. Renew Sustain Energy Rev 73:730–744

    Article  Google Scholar 

  21. Surender D, Halimi MA, Khan T, Talukdar FA, Antar YMM (2022) A 90° twisted quarter-sectored compact and circularly polarized DR-Rectenna for RF energy harvesting applications. IEEE Antennas Wirel Propag Lett 21:1139–1143. https://doi.org/10.1109/LAWP.2022.3159482

    Article  Google Scholar 

  22. Chen Q, Xiong Q (2020) Garbage classification detection based on improved YOLOV4. J Comput Commun 08:285–294. https://doi.org/10.4236/jcc.2020.812023

    Article  Google Scholar 

  23. Jadhav R, Anand DS, Gupta AK, Khare S, Sharma D, Tapadiya P (2021) Real-time object detection for visually challenged. In: Machine learning and information processing. Springer, pp 281–296

    Google Scholar 

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Correspondence to Tasardhik Basha Shaik .

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Shaik, T.B., Mal, R. (2024). Algorithm to Assist Visually Impaired Person for Object Detection in Real Time. In: Gabbouj, M., Pandey, S.S., Garg, H.K., Hazra, R. (eds) Emerging Electronics and Automation. E2A 2022. Lecture Notes in Electrical Engineering, vol 1088. Springer, Singapore. https://doi.org/10.1007/978-981-99-6855-8_12

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