Project description: The utilization of drone technology offers promising avenues for enhancing traffic safety enforcement through advanced aerial computer vision techniques. This project explores the potential of drones in improving traffic safety through various applications, including speed limit enforcement, illegal parking detection, accident scene monitoring, and congestion management. We discuss the benefits, challenges, and future prospects of utilizing drones for traffic safety enforcement, highlighting the potential to enhance road safety, reduce accidents, and improve overall traffic management efficiency. This study aims to achieve the following objectives:
a. Automatic detection of helmets worn by two-wheeler riders and identification of their vehicle number plates for surveillance purposes.
b. Tracking vehicles through object detection methods and implementing lightweight deep learning algorithms.
c. Collection of aerial imaging datasets tailored for object detection tasks, thereby facilitating research and development in this domain.
Through the integration of drone technology and deep learning algorithms, this research endeavors to revolutionize traffic safety enforcement practices, leading to improved road safety and compliance.
Platform: Python and PyTorch
Output:
Drones Designed:
