Traffic Violation Detection System

The Traffic Violation Detection System is designed to accurately identify and classify various types of traffic violations, such as helmet non-usage, red light running, overspeeding, and lane violations. It employs advanced computer vision algorithms and AI-based models to enhance accuracy and reliability in violation detection. The system aims to automate the monitoring and processing of traffic violations, thereby improving enforcement efficiency and enhancing road safety.


The client faced challenges in managing traffic violations effectively. They needed a solution to detect various violations using computer vision, including helmet detection, zebra crossing violations, lane line deviations, one-wheel driving, and smoky vehicles.


We proposed a solution based on computer vision technology to address the client’s needs. Using advanced algorithms and deep learning models, we created a system capable of accurately detecting various types of traffic violations in real-time. Specifically, the system was designed to identify instances such as individuals not wearing helmets while riding motorcycles, ensuring adherence to safety regulations. Additionally, the system could detect violations like zebra crossing infringements, lane line deviations, one-wheel driving, and the presence of smoky vehicles.

Features & Benefits

Comprehensive Violation Monitoring

Helmet Detection, Speeding, Illegal Crossing, and Lane Violations

The system ensures thorough enforcement of traffic regulations by monitoring various types of violations.

Integration of Advanced Technologies

Computer Vision and Machine Learning Algorithms

The system accurately detects and tracks objects such as vehicles, cyclists, and pedestrians, enhancing surveillance capabilities.

Real-time Alerts and Notifications

Instant Alerts

Users receive immediate notifications regarding detected violations, enabling prompt action by law enforcement authorities and promoting compliance among road users.

Enhanced Safety Measures

Identification of Risky Behaviors

By identifying actions such as reckless driving or failure to wear helmets, the system contributes to improving road safety and reducing the risk of accidents and injuries.

Continuous Improvement

Regular Model Updates

The system’s models undergo regular updates and refinements to enhance accuracy and performance, ensuring it remains effective in addressing evolving traffic challenges.

Data-driven Decision Making

Detailed Analytics and Insights

By providing comprehensive data on traffic violations and patterns, the system enables authorities to make informed decisions and implement targeted interventions for improving traffic management and safety.

Technologies Used to Build the Solution

PyTorch, Python, Object Detection, Object Tracking