Title:
Deep Learning-based Approach for Detecting Traffic Violations Involving No Helmet Use and Wrong Cycle Lane Usage
Authors:
Shailendra Singh Kathait – Co-Founder & Chief Data Scientist, Ashish Kumar – Principal Data Scientist, Samay Sawal – Intern Data Scientist, Ram Patidar – Data Scientist, Khushi Agrawal – Intern Data Scientist [all Valiance Solutions Noida, India]
Summary:
Urban road safety is significantly compromised by traffic violations such as motorcyclists riding without helmets and unauthorized use of cycle lanes. This study introduces a deep learning-based framework designed for the automated, real-time detection of these specific infractions. By leveraging advanced object detection and tracking algorithms, notably the YOLO (You Only Look Once) architecture, combined with spatial reasoning techniques, the system effectively identifies motorcyclists without helmets and detects bicycles operating outside designated lanes. Enhancements like bounding box adjustments, centroid-based relationships, and region-specific filtering are employed to improve detection accuracy. Additional analyses, including speed and direction assessments, provide contextual understanding of the violations. The system offers visual feedback and maintains cumulative violation counts, demonstrating robust performance across diverse urban traffic scenarios. Its scalable architecture allows for extension to detect a broader range of traffic violations, aiming to reduce reliance on manual monitoring and bolster road safety enforcement.
