Bike riders have been rapidly increasing amid time in various countries. Motorbikes are favoured by citizens belonging to different classes of the society due to many reasons such as its economic value. Wearing helmets is compulsory according to the standard however the vast majority avoid it. A principal goal of the helmet is to guarantee the safety of the riders.
In this work, we aim to automatically and accurately detect whether a person is wearing a helmet while riding a motorcycle. Our motivation is to promote road safety and ultimately help reduce the number of motorcycle accidents. To this end, we have annotated a data set of 25000 images that contain people riding motorcycles with or without helmets.

Separating Riders from Images

The purpose of this project is to introduce a new method for helmet detecting in video footage. This new method is based on the use of bounding boxes. By using this method, it is possible to separate the user from the background and distinguish them from other objects in the footage.

Tools used to make this happen

We use data annotation tool called Plainsight to annotate all the datasets. The tool offers different and efficient annotation methods to completely transform any dataset to be used for machine learning algorithms. The methods available are:

• Bounding Box Annotation: Marking rectangular boxes to identify targeted objects.
• Semantic Segmentation: Annotating images after segmenting into component parts.
• Polygon Annotation: Annotating an object’s exact edges, regardless of shape.
• Object Tracking: Locating and tracking objects across a series of images or point clouds in sequential frames.

Conclusion

In conclusion, the use of bounding boxes for helmet detection can be an effective method for detecting people wearing helmets in video footage. This method can help to improve road safety and reduce the number of motorcycle accidents.
This method has the potential to accurately detect whether a person is wearing a helmet while riding a motorcycle. The use of bounding boxes also has the advantage of being able to separate the user from the background and distinguish them from other objects in the footage.

About Data Labeler

By leveraging the advanced tools and technologies, Data Labeler offers best-in-class data labeling services in computer Vision projects. We at Data labeler believe in providing jobs to underserved communities and making them financially independent. We are on a mission to help them earn a living through the major changes brought by AI & ML, empowering businesses all over the world.
Increase your competitive advantage with unlimited support and exponential growth through our Data Annotation Services.