Enhancing Mobility and Independence of Visually Impaired Individuals through Mobile-Based Real-Time Obstacle Detection Systems

Independent mobility is a critical determinant of social health and Quality of Life for individuals with visual impairments, yet physical barriers and limitations in traditional aids often lead to restricted travel, contributing significantly to loneliness, social exclusion, and heightened risks of depression and anxiety. This paper systematically analyzes the development and implementation challenges of Mobile-Based Real-Time Obstacle Detection Systems as a pivotal technological intervention designed to overcome these barriers. Successful RT-ODS relies on highly optimized technical architectures, such as the lightweight YOLOv8 deep learning model, tailored for efficient real-time inference on resource-constrained mobile platforms. Empirical evidence demonstrates the feasibility of achieving robust performance, with some systems attaining an accuracy greater than 90% and a mAP less than 0.5, under varying environmental conditions. Crucially, the adoption and long-term efficacy of these systems are contingent upon addressing socio-economic and ethical constraints. User-centric design requires integrating multimodal feedback (auditory and haptic), while economic accessibility demands low production costs to serve a population often facing financial vulnerability. This synthesis concludes that Real-Time Obstacle Detection Systems, when developed with a comprehensive interdisciplinary approach that balances technical optimization, cost-effectiveness, and rigorous ethical compliance, offers a viable, scalable pathway to significantly enhance the confidence, independence, and social integration of visual impairments individuals.

Visual Impairment, Social Exclusion, Object Detection

1. Tshuma, C., Ntombela, N., and van Wijk, H. C., (2022). Challenges and coping strategies of the visually impaired adults: A brief exploratory systematic literature review. Prizren Social Science Journal, 6(2), 71–80. [Google Scholar] [Crossref]

2. Remillard, E. T., Koon, L. M., Mitzner, T. L., and Rogers, W. A., (2024). Everyday challenges for individuals aging with vision impairment: Technology implications. Gerontologist, 64(6), gnad169. [Google Scholar] [Crossref]

3. Purola, P., Koskinen, S., and Uusitalo, H., (2023). Impact of vision on generic health related quality of life A systematic review. Acta Ophthalmol, 101(7), 717–728. [Google Scholar] [Crossref]

4. [4] Bastidas-Guacho, G. K., Paguay Alvarado, M. A., Moreno-Vallejo, P. X., Moreno-Costales, P. R., Ocaña Yanza, N. S., and Troya Cuestas, J. C., (2025). Computer Vision-Based Obstacle Detection Mobile System for Visually Impaired Individuals. Multimodal Technologies and Interaction, 9(5), 48. [Google Scholar] [Crossref]

5. Khan, A., and Khusro, S., (2021). An insight into smartphone-based assistive solutions for visually impaired and blind people: issues, challenges and opportunities. Univers Access Inf Soc, 20(2), 265–298. [Google Scholar] [Crossref]

6. Al-Razgan, M., et al., (2021). A systematic literature review on the usability of mobile applications for visually impaired users. PeerJ Comput Sci, 7, e771. [Google Scholar] [Crossref]

7. Collart, L., Ortibus, E., and Ben Itzhak, N., (2024). An evaluation of health-related quality of life and its relation with functional vision in children with cerebral visual impairment. Res Dev Disabil, 154, 104861. [Google Scholar] [Crossref]

8. Wang, J., Li, Y., Yang, G.-Y., and Jin, K., (2024). Age-related dysfunction in balance: a comprehensive review of causes, consequences, and interventions. Aging Dis, 16(2), 714. [Google Scholar] [Crossref]

9. Singh, R. R., and Maurya, P., (2022). Visual impairment and falls among older adults and elderly: evidence from longitudinal study of ageing in India. BMC Public Health, 22(1), 2324. [Google Scholar] [Crossref]

10. Ouyang, S., et al., (2022). Risk factors of falls in elderly patients with visual impairment. Front Public Health, 10, 984199. [Google Scholar] [Crossref]

11. Kim, H., and Sohn, D., (2020). The urban built environment and the mobility of people with visual impairments: analysing the travel behaviours based on mobile phone data. Journal of Asian Architecture and Building Engineering, 19(6), 731–741. [Google Scholar] [Crossref]

12. Thoma, M., Partaourides, H., Sreedharan, I., Theodosiou, Z., Michael, L., and Lanitis, A., (2023). Performance Assessment of Fine-Tuned Barrier Recognition Models in Varying Conditions. In: International Conference on Computer Analysis of Images and Patterns, Springer, 172–181. [Google Scholar] [Crossref]

13. Alsuwaylimi, A. A., Alanazi, R., Alanazi, S. M., Alenezi, S. M., Saidani, T., and Ghodhbani, R., (2024). Improved and efficient object detection algorithm based on YOLOv5. Engineering, Technology & Applied Science Research, 14(3), 14380–14386. [Google Scholar] [Crossref]

14. Jia, X., Tong, Y., Qiao, H., Li, M., Tong, J., and Liang, B., (2023). Fast and accurate object detector for autonomous driving based on improved YOLOv5. Sci Rep, 13(1), 9711. [Google Scholar] [Crossref]

15. Rasheed, A. F., and Zarkoosh, M., (2025). Optimized YOLOv8 for multi-scale object detection. J Real Time Image Process, 22(1), 6. [Google Scholar] [Crossref]

16. Said, Y., Atri, M., Albahar, M. A., Ben Atitallah, A., and Alsariera, Y. A., (2023). Obstacle detection system for navigation assistance of visually impaired people based on deep learning techniques. Sensors, 23(11), 5262. [Google Scholar] [Crossref]

17. Hanhirova, J., Kämäräinen, T., Seppälä, S., Siekkinen, M., Hirvisalo, V., and Ylä-Jääski, A., (2018). Latency and throughput characterization of convolutional neural networks for mobile computer vision. In: Proceedings of the 9th ACM Multimedia Systems Conference, 204–215. [Google Scholar] [Crossref]

18. Mahendran, J. K., Barry, D. T., Nivedha, A. K., and Bhandarkar, S. M., (2021). Computer vision-based assistance system for the visually impaired using mobile edge artificial intelligence. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2418–2427. [Google Scholar] [Crossref]

19. Kaliappan, V. K., M. S. V., Shanmugasundaram, K., Ravikumar, L., and Hiremath, G. B., (2023). Performance Analysis of YOLOv8, RCNN, and SSD Object Detection Models for Precision Poultry Farming Management. In: 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), 1–6. doi: 10.1109/AESPC59761.2023.10389906. [Google Scholar] [Crossref]

20. Sonu, B., Singh, Ajay, and Sharma, A., (2024). A Comparative Study of YOLOv8, Faster R-CNN, and SSD in Traffic Sign Detection with Consideration of GPS and Central Feedback. In: 3rd International Conference on Advances in Computing, Communication and Materials, ICACCM 2024, IEEE. doi: 10.1109/ICACCM61117.2024.11059135. [Google Scholar] [Crossref]

21. Wang, Y., Wang, K., Zhang, Z., Boydens, J., Pissoort, D., and Verbeke, M., (2024). Navigating the Waters of Object Detection: Evaluating the Robustness of Real-time Object Detection Models for Autonomous Surface Vehicles. In: Proceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024, IEEE, 985–992. doi: 10.1109/CAI59869.2024.00180. [Google Scholar] [Crossref]

22. Ling, A. L. I., Xiang, G. Y., Bingi, K., Omar, M., and Ibrahim, R., (2024). Review of Machine Learning-Based Techniques for Detecting Specific and General Objects. In: IET Conference Proceedings, IET, 119–124. doi: 10.1049/icp.2025.0244. [Google Scholar] [Crossref]

23. Shobaki, W. A., and Milanova, M., (2025). A Comparative Study of YOLO, SSD, Faster R-CNN, and More for Optimized Eye-Gaze Writing. Sci, 7(2). doi: 10.3390/sci7020047. [Google Scholar] [Crossref]

24. Dharma, A. S., Pardosi, C. N. S., and Silaen, Z. P., (2025). Comparative Performance of Yolov8 and SSD-mobilenet Algorithms for Road Damage Detection in Mobile Applications. sinkron, 9(3), 1159–1169. doi: 10.33395/sinkron.v9i3.15008. [Google Scholar] [Crossref]

25. Yeong, D. J., Velasco-Hernandez, G., Barry, J., and Walsh, J., (2021). Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors, 21(6), 2140. [Google Scholar] [Crossref]

26. Nagy, M., and Lăzăroiu, G., (2022). Computer vision algorithms, remote sensing data fusion techniques, and mapping and navigation tools in the Industry 4.0-based Slovak automotive sector. Mathematics, 10(19), 3543. [Google Scholar] [Crossref]

27. Ghaffari, G., Tagaro Andersson, A., Hallberg, P., and Saremi, A., (2025). An assistive haptic-based obstacle avoidance system for individuals with profound visual impairment. Cogent Eng, 12(1), 2560974. [Google Scholar] [Crossref]

28. Căilean, A.-M., Avătămăniței, S.-A., Beguni, C., Zadobrischi, E., Dimian, M., and Popa, V., (2023). Visible light communications-based assistance system for the blind and visually impaired: design, implementation, and intensive experimental evaluation in a real-life situation. Sensors, 23(23), 9406. [Google Scholar] [Crossref]

29. Patil, K., Jawadwala, Q., and Shu, F. C., (2018). Design and construction of electronic aid for visually impaired people. IEEE Trans Hum Mach Syst, 48(2), 172–182. [Google Scholar] [Crossref]

30. Senjam, S. S., Manna, S., and Bascaran, C., (2021). Smartphones-based assistive technology: Accessibility features and apps for people with visual impairment, and its usage, challenges, and usability testing. Dove Medical Press Ltd. doi: 10.2147/OPTO.S336361. [Google Scholar] [Crossref]

31. Wittich, W., Boie, N. R., and Jaiswal, A., (2023). Methodological Approaches to Obtaining Informed Consent when Conducting Research with Individuals with Deafblindness. Int J Qual Methods, 22. doi: 10.1177/16094069231205176. [Google Scholar] [Crossref]

32. Akter, T., Ahmed, T., Kapadia, A., and Swaminathan, M., (2022). Shared privacy concerns of the visually impaired and sighted bystanders with camera-based assistive technologies. ACM Transactions on Accessible Computing (TACCESS), 15(2), 1–33. [Google Scholar] [Crossref]

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