Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view

Estimating helmet wearing rates via a scalable, low-cost algorithm: a novel integration of deep learning and google street view

Blog Article

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 Steamrollers 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 Left Side Cover the availability of helmet usage data resulting from this approach could bolster progress tracking and facilitate evidence-based policymaking for helmet wearing globally.