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.

通过训练和评估 YOLOv8 和 RT-DETR 模型的不同版本，我们的研究项目旨在创建和验证一个先进的深度学习框架，能够处理复杂视觉输入，以实时识别各种环境中的汽车和行人。YOLOv8 Large 版本被证明是最有效的，在行人识别方面具有很高的准确性和鲁棒性。研究结果表明，该模型能够显著提高交通监控和安全性，成为计算机视觉领域实时可靠检测的重要贡献，并为交通管理系统建立了新的基准。