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.
Original language | English (US) |
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Article number | 1645 |
Journal | BMC public health |
Volume | 24 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2024 |
Keywords
- Deep learning
- Google Street View
- Helmet
- Low-cost and scalable algorithm
- Motorcyclists
ASJC Scopus subject areas
- Public Health, Environmental and Occupational Health