Real-Time Detection and Analysis of Vehicles and Pedestrians using Deep
  Learning
Md Nahid Sadik Tahmim Hossain Faisal Sayeed
Computer vision, particularly vehicle and pedestrian identification is
critical to the evolution of autonomous driving, artificial intelligence, and
video surveillance. Current traffic monitoring systems confront major
difficulty in recognizing small objects and pedestrians effectively in
real-time, posing a serious risk to public safety and contributing to traffic
inefficiency. Recognizing these difficulties, our project focuses on the
creation and validation of an advanced deep-learning framework capable of
processing complex visual input for precise, real-time recognition of cars and
people in a variety of environmental situations. On a dataset representing
complicated urban settings, we trained and evaluated different versions of the
YOLOv8 and RT-DETR models. The YOLOv8 Large version proved to be the most
effective, especially in pedestrian recognition, with great precision and
robustness. The results, which include Mean Average Precision and recall rates,
demonstrate the model's ability to dramatically improve traffic monitoring and
safety. This study makes an important addition to real-time, reliable detection
in computer vision, establishing new benchmarks for traffic management systems.