The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Identifying universal safety signs using computer vision for an assistive feedback mobile application
Abstract
When walking on the road or navigating unfamiliar areas, the elderly, visually handicapped, and persons with hearing loss encounter challenges. The information on numerous safety signs such as road signs, traffic signs, workplace safety signs or industrial hazard signs is usually unhelpful to them. This paper proposes a solution that can detect a comprehensive set of safety signs in real-time. Our app runs a deep learning model pre-trained on a custom-built dataset. The deep learning model we have used is explicitly built for object detection to find regions of interest, create appropriate bounding boxes, and classify the signs with three different levels of severity - Danger, Caution, and Prohibitory. The camera-equipped smartphone relays haptic or audio feedback upon the successful detection of a safety sign.
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These previous studies have found many successes of the technology.
“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 [66]
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.
“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 [66]
Citations
Cites
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
Flagged first, then by citations
Research works printed but not linked: 23
- he, 2015, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904, 10.1109/TPAMI.2015.2389824 · DOI · Paper page
- redmon, 2018, Yolov3: An incremental improvement, CoRR, abs 1804 2767
- 10.1109/CVPRW50498.2020.00203 · DOI · Paper page
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- technologies, 2020, Road signs datasets
- mohamed, 2018, Hazmat label dataset - seeing signs of danger
- skalski, 0, Make Sense
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- mishra, 0, Safety Signs Dataset
- 0, Lite module loader
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- 10.1109/ICCV.2015.169 · DOI · Paper page
- everingham, 0, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Cited by
Sorted by citations
An investigation into the effectiveness of using acoustic touch to assist people who are blind
Zhu, H. Y., Hossain, S. N., Jin, C., Singh, A. K., Nguyen, M. T. D., Deverell, L., Nguyen, V., Gates, F. S., Fernandez, I. G., Melencio, M. V., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0290431 · Cited by 16
Metadata
- Authors
- Mishra, A., Raj, N., Bodhe, S., & Bajwa, G.
- Affiliations
- Lakehead University (CA)
- Venue
- 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI)
- Publisher
- IEEE
- Published
- 2022-08
- Volume
- 37
- Pages
- 285-290
- OpenAlex
- W4297684246
- Cited by
- 1
- References
- 1
- Citations by year
- 2023: 1
- FWCI
- 0.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Information Reuse and Integration (IRI) (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 › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Keywords
- object detection, assistive technology, mobile application, haptic feedback, audio feedback, older adults, visually impaired people, hearing-impaired people, traffic signs, real-time detection, smartphone camera, severity classification
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