AUTOMATED HELMET DETECTION AND CITATION SYSTEM USING LICENSE PLATE RECOGNITION

Abstract

The rising affordability of motorcycles has made them an increasingly popular choice for daily transportation. While this trend offers many benefits, including reduced commuting costs and enhanced mobility, it has also led to a concerning increase in motorcycle accidents. A significant factor contributing to these incidents is the alarming number of riders who neglect to wear helmets. This oversight not only endangers the lives of motorcyclists but also poses a broader risk to public safety on our roads.
In response to this critical issue, governments have implemented laws designating it a punishable offense to ride without a helmet. Although existing video surveillance systems have been useful in monitoring compliance, they rely heavily on human intervention. This dependence can lead to inefficiencies over time, as well as potential biases in enforcement, which undermine the effectiveness of these measures.
To combat these challenges, we propose an innovative solution: an automated system designed for real-time detection of motorcyclists riding without helmets through advanced surveillance video analysis. Our approach encompasses several key components:
1.
Motorcycle Detection: We utilize background subtraction techniques combined with machine learning algorithms to accurately identify motorcycles within surveillance footage. This allows for precise monitoring of areas with high traffic volumes.
2.
Helmet Classification: Upon detecting a motorcycle, we implement advanced edge detection algorithms—leveraging both first-order and second-order derivatives—to ascertain whether the rider is wearing a helmet. This classification process is further enhanced using deep learning neural network models, which significantly improve the accuracy of our detections by minimizing false positives and negatives.
3.
License Plate Recognition: If a rider is identified as not wearing a helmet, our system employs Optical Character Recognition (OCR) alongside neural networks to capture and recognize the vehicle's license plate. This capability ensures that offenders can be traced and held accountable.
4.
Evidence Collection: Beyond simply identifying violations, our system archives frames from the video where the rider is seen without a helmet.
These frames serve as irrefutable digital evidence for enforcement purposes, aiding in the prosecution of non-compliant riders.
By automating this detection process, our system empowers governmental authorities to efficiently issue fines to those who fail to comply with helmet laws. This not only ensures accountability but also promotes saferriding practices among motorcyclists. Furthermore, it communicates to the community that vigilant monitoring is ongoing, even during off-peak hours, thus enhancing the perceived risk of non-compliance.
In conclusion, our proposed solution marks a significant advancement in road safety technology. By integrating real-time monitoring with automated enforcement mechanisms, we can better protect motorcyclists and foster a culture of compliance with safety regulations. Ultimately, this initiative has the potential to reduce accident rates, save lives, and create a safer environment for all road users.