The DOI matches its Crossref record; 12 of the 13 references it cites were found in a registry or an index.
Towards Outdoor Navigation System for Visually Impaired People using YOLOv5
Abstract
Navigation is one of the many difficulties the Blind and Visually Impaired (BVI) face, be it indoor or outdoor. BVI people use their remaining senses such as smell, touch, and memory to navigate in a familiar environment. But the real problem begins when the environment is new. In the new environment, they seek help from closed ones or use assistive tools. This paper proposes a deep learning based outdoor smartphone navigation system approach which will assist the user in crossing the road by detecting the crosswalks from a mobile device. CNN of Deep learning has been proven to give the best results for image classification. Hence we want to analyze and compare different variants of CNN. As mobile devices have computed limitations, we explore two lightweight state-of-the-art neural networks, namely YOLOv5 and MobileNetSSDV2, for this task. The image dataset of crosswalks for work is collected via web scraping and manual effort. To evaluate the performance of the two neural networks: mAP, loss, and training time of the models are compared. As a result, it is determined that YOLOv5 has a five times faster inference speed than MobileNetSSDv2, which makes YOLOv5 more suitable for real-time applications. This approach utilizes the smartphone camera to detect the crosswalks and provide voice feedback to help people with low vision understand their environment.
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In the citing paper
“Multiple similar previous works [64–67] have explored using the YOLOv5 (or similar) deep learning model on a smartphone for the computer vision of smart glasses.”
From An investigation into the effectiveness of using acoustic touch to assist people who are blind (2023) · cites this paper as [65]
In the citing paper
“These studies have found many successes of the technology.”
From An investigation into the effectiveness of using acoustic touch to assist people who are blind (2023) · cites this paper as [65]
Citations
Cites
MobileNetV2: Inverted Residuals and Linear Bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00474 · Cited by 28,755
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017) · arXiv · doi:10.48550/arxiv.1704.04861 · Cited by 19,015
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
Bourne, R. R. A., Flaxman, S. R., Braithwaite, T., Cicinelli, M. V., Das, A., Jonas, J. B., Keeffe, J., Kempen, J. H., Leasher, J., Limburg, H., et al. (2017) · The Lancet Global Health · doi:10.1016/s2214-109x(17)30293-0 · Cited by 2,213
A Survey of Deep Learning-Based Object Detection
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,454
Navigation Systems for the Blind and Visually Impaired: Past Work, Challenges, and Open Problems
Real, S., & Araújo, Á. (2019) · Sensors · doi:10.3390/s19153404 · Cited by 225
Mobilenet-SSDv2: An Improved Object Detection Model for Embedded Systems
Chiu, Y., Tsai, C., Ruan, M.-D., Shen, G.-Y., & Lee, T. (2020) · International Conference on System Science and Engineering · doi:10.1109/icsse50014.2020.9219319 · Cited by 206
An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
Velázquez, R., Pissaloux, E. E., Rodrigo, P. M., Carrasco, M., Giannoccaro, N. I., & Lay-Ekuakille, A. (2018) · Applied Sciences · doi:10.3390/app8040578 · Cited by 104
NavTU: Android Navigation App for Thai People with Visual Impairments
Somyat, N., Wongsansukjaroen, T., Longjaroen, W., & Nakariyakul, S. (2018) · International Conference on Knowledge and Smart Technology · doi:10.1109/kst.2018.8426068 · Cited by 16
Towards Intelligent Assistive System for Visually Impaired People: Outdoor Navigation System
Gamal, O., Thakkar, S., & Roth, H. (2020) · International Conference on System Theory, Control and Computing (ICSTCC) · doi:10.1109/icstcc50638.2020.9259682 · Cited by 15
Smartphone Based Outdoor Navigation and Obstacle Avoidance System for the Visually Impaired
Chen, Q., Wu, L., Chen, Z., Lin, P., Cheng, S., & Wu, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33709-4_3 · Cited by 14
Divya-Dristi: A Smartphone based Campus Navigation System for the Visually Impaired
Dutta, S., Barik, M. S., Chowdhury, C., & Gupta, D. (2018) · International Conference on Emerging Applications of Information Technology · doi:10.1109/eait.2018.8470397 · Cited by 14
Research works printed but not linked: 3
- nelson, 2020, YOLOv5 is Here, State-of-the-Art Object Detection at 140 FPS
- jiang, 2020, Real-time object detection method for embedded devices, In Computer Vision and Pattern Recognition
- shadi, 2019, Outdoor navigation for visually impaired based on deep learning, In Proc CEUR Workshop Proc, 2514, 97
Cited by
Sorted by citations
Madake, J., Bhatlawande, S. S., Solanke, A., & Shilaskar, S. (2023) · IEEE Access · doi:10.1109/access.2023.3291074 · Cited by 52
IBGS: A Wearable Smart System to Assist Visually Challenged
Xia, K., Li, X., Liu, H., Zhou, M., & Zhu, K. (2022) · IEEE Access · doi:10.1109/access.2022.3193097 · Cited by 39
A Smart Cane Based on 2D LiDAR and RGB-D Camera Sensor-Realizing Navigation and Obstacle Recognition
Mai, C., Chen, H., Zeng, L., Li, Z., Liu, G., Qiao, Z., Qu, Y., Li, L., & Li, L. (2024) · Sensors · doi:10.3390/s24030870 · Cited by 36
An AIoT-Based Assistance System for Visually Impaired People
Li, J., Xie, L., Chen, Z., Shi, L., Chen, R., Ren, Y., Wang, L., & Lu, X. (2023) · Electronics · doi:10.3390/electronics12183760 · Cited by 34
Theodorou, P., Tsiligkos, K., & Μηλιώνης, Α. (2023) · Sensors · doi:10.3390/s23125411 · Cited by 26
Metadata
- Authors
- Chandna, S., & Singhal, A.
- Affiliations
- SRH University Heidelberg,Faculty of Information, Media, and Design,Heidelberg,Germany
- Amity University (IN)
- Venue
- 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)
- Publisher
- IEEE
- Published
- 2022-01-27
- Pages
- 617-622
- OpenAlex
- W4220877488
- Cited by
- 17
- References
- 13
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 9 · 2022: 1
- FWCI
- 1.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Cloud Computing, Data Science & Engineering (Confluence) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Health Professions › Occupational Therapy › Assistive Technology in Communication and Mobility
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- outdoor navigation, visually impaired people, blind people, crosswalk detection, YOLOv5, lightweight neural networks, smartphone camera, voice feedback, assistive technology, real-time object detection, image dataset collection
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