Wearing Helmet is a critical safety measure not only for riders but also for passengers. However, people often tend to skip wearing these protective headgears, thereby leading to, an increased risk of injury or death in the event of an accident. There is a growing necessity to develop innovative methods that automatically monitor and prevent unsafe driving. To address this issue, we have developed a computer vision-based helmet detection system that can detect if a rider has his helmet on in real-time.
This paper presents a comprehensive review of the state-of-the-art computer vision-based helmet detection systems for two-wheelers. The review covers various aspects of helmet detection, including image pre-processing, feature extraction, and classification. The strengths and limitations of existing approaches are analyzed, while proposing potential directions for future research. The results demonstrate that computer vision-based helmet detection systems hold significant potential to reduce the risk of accidents and improve safety for riders.
Helmets are a crucial safety measure for riders and passengers on two-wheelers. Unfortunately, many individuals neglect to wear helmets, leading to an increased risk of severe head injuries or fatalities in the event of an accident. There is a growing need for innovative approaches that can automatically monitor and prevent unsafe driving behaviours. To address this challenge, a computer vision-based helmet detection system has been developed that can detect in real-time whether a rider is wearing a helmet.
This paper presents a comprehensive review of the current state-of-the-art in computer vision-based helmet detection systems for two-wheelers. The review covers various aspects of helmet detection, including image pre-processing, feature extraction, and classification. The strengths and limitations of existing approaches are analysed, and potential future research directions are proposed.
Image pre-processing is an essential component of helmet detection systems, as it allows for the removal of irrelevant information and the enhancement of relevant image features. Techniques such as filtering, segmentation, and morphological operations can be used to pre-process images before further analysis.
Feature extraction is the process of identifying and selecting key image features that can be used to classify whether a rider is wearing a helmet or not. The most used features in helmet detection systems include colour, texture, and shape.
Classification is the final step in the helmet detection process, where a machine learning algorithm is used to determine whether the input image contains a helmet or not. Various classification methods, such as Support Vector Machines (SVMs), Random Forests, and Convolutional Neural Networks (CNNs) have been applied to helmet detection systems with varying degrees of success.
While existing helmet detection systems have demonstrated promising results, there are still several challenges that need to be addressed. These include issues related to occlusion, lighting, and variations in head pose and helmet type. Future research directions should focus on developing more robust and accurate helmet detection systems that can be deployed in real-world scenarios.
In conclusion, computer vision-based helmet detection systems hold significant potential for reducing the risk of accidents and improving safety for riders on two-wheelers. By addressing the challenges associated with helmet detection, these systems can play a vital role in promoting safe driving behaviours and preventing unnecessary injuries and fatalities.