Bibliographic Details
| Title: |
Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view |
| Authors: |
Qingfeng Li, Xianglong Wang, Abdulgafoor M. Bachani |
| Source: |
BMC Public Health, Vol 24, Iss 1, Pp 1-7 (2024) |
| Publisher Information: |
BMC, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Public aspects of medicine |
| Subject Terms: |
Helmet, Motorcyclists, Deep learning, Google Street View, Low-cost and scalable algorithm, Public aspects of medicine, RA1-1270 |
| More Details: |
Abstract Introduction Wearing a helmet reduces the risk of head injuries substantially in the event of a motorcycle crash. Countries around the world are committed to promoting helmet use, but the progress has been slow and uneven. There is an urgent need for large-scale data collection for situation assessment and intervention evaluation. Methods This study proposes a scalable, low-cost algorithm to estimate helmet-wearing rates. Applying the state-of-the-art deep learning technique for object detection to images acquired from Google Street View, the algorithm has the potential to provide accurate estimates at the global level. Results Trained on a sample of 3995 images, the algorithm achieved high accuracy. The out-of-sample prediction results for all three object classes (helmets, drivers, and passengers) reveal a precision of 0.927, a recall value of 0.922, and a mean average precision at 50 (mAP50) of 0.956. Discussion The remarkable model performance suggests the algorithm’s capacity to generate accurate estimates of helmet-wearing rates from an image source with global coverage. The significant enhancement in the availability of helmet usage data resulting from this approach could bolster progress tracking and facilitate evidence-based policymaking for helmet wearing globally. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1471-2458 |
| Relation: |
https://doaj.org/toc/1471-2458 |
| DOI: |
10.1186/s12889-024-19118-0 |
| Access URL: |
https://doaj.org/article/0360041fbb0a4de39c84833e94acb840 |
| Accession Number: |
edsdoj.0360041fbb0a4de39c84833e94acb840 |
| Database: |
Directory of Open Access Journals |
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