The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Falls from heights: A computer vision-based approach for safety harness detection
Abstract
Abstract Falls from heights (FFH) are major contributors of injuries and deaths in construction. Yet, despite workers being made aware of the dangers associated with not wearing a safety harness, many forget or purposefully do not wear them when working at heights. To address this problem, this paper develops an automated computer vision-based method that uses two convolutional neural network (CNN) models to determine if workers are wearing their harness when performing tasks while working at heights. The algorithms developed are: (1) a Faster-R-CNN to detect the presence of a worker; and (2) a deep CNN model to identify the harness. A database of photographs of people working at heights was created from activities undertaken on several construction projects in Wuhan, China. The database was then used to test and train the developed networks. The precision and recall rates for the Faster R-CNN were 99% and 95%, and the CNN models 80% and 98%, respectively. The results demonstrate that the developed method can accurately detect workers not wearing their harness. Thus, the computer vision-based approach developed can be used by construction and safety managers as a mechanism to proactively identify unsafe behavior and therefore take immediate action to mitigate the likelihood of a FFH occurring.
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Cites
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Cited by
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103517 · Cited by 1,336
Computer vision techniques in construction: A critical review
Xu, S., Wang, J., Shou, W., Ngo, T., Sadick, A.-M., & Wang, X. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09504-3 · Cited by 409
Computer vision for behaviour-based safety in construction: A review and future directions
Fang, W., Love, P. E. D., Luo, H., & Ding, L. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100980 · Cited by 346
Self-citationThe classification of construction waste material using a deep convolutional neural network
Davis, P., Aziz, F., Newaz, M. T., Sher, W., & Simon, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103481 · Cited by 204
Literature review of digital twins applications in construction workforce safety
Hou, L., Wu, S., Zhang, G. (., Tan, Y., & Wang, X. (2020) · Applied Sciences · doi:10.3390/app11010339 · Cited by 194
Metadata
- Authors
- Fang, W., Ding, L., Luo, H., & Love, P. E. D.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 91
- Pages
- 53-61
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W2790722345
- Cited by
- 450
- References
- 8
- FWCI
- 17.57
- Type
- journal article
- Language
- en
- Open access
- closed
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- Fang, W., Ding, L., Luo, H., & Love, P. E. D. (2018). Falls from heights: A computer vision-based approach for safety harness detection. Automation in Construction. https://doi.org/10.1016/j.autcon.2018.02.018
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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