A Coarse-to-Fine Two-Stage Helmet Detection Method for Motorcyclists

Abstract

In recent years, motorcycle accidents have occurred frequently, with a important reason being that motorcyclists do not wear helmets properly. The visual method of detecting whether a motorcyclist is wearing helmet based on monitoring videos can provide technical support for traffic management. However, the appearance characteristics of motorcycle drivers and passengers are too similar to distinguish, which makes it difficult to detect helmet. In this task, we propose a Coarse-to-fine Two-stage Helmet Detection Method for Motorcyclists to improve the accuracy of helmet and motorcyclist detection. Our Coarse detector detect the rough location of people and motorcycle as the initial suggestion for the following Fine-grained detection. Then our Fine-grained detector employs a classification branch to accurately distinguish between the driver and passengers. Finally, we use some useful strategies such as Test Time Augmentation (TTA) and Weighted Boxes Fusion (WBF) to achieve further improvements to our proposed framework. Our proposed framework achieved mAP score of 39.4 % on the test dataset of AI City Challenge 2024 Track5.