@article{ref1,
title="Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and Google Street View",
journal="BMC public health",
year="2024",
author="Li, Qingfeng and Wang, Xianglong and Bachani, Abdulgafoor M.",
volume="24",
number="1",
pages="e1645-e1645",
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. <br><br>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. <br><br>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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="1471-2458",
doi="10.1186/s12889-024-19118-0",
url="http://dx.doi.org/10.1186/s12889-024-19118-0"
}