Abstract
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.
Abstract (translated)
计算机视觉，特别是车辆和行人识别，对自动驾驶、人工智能和视频监控技术的演变至关重要。目前，实时交通监测系统在实时识别小型物体和行人方面面临重大困难，这给公共安全和交通效率带来了严重威胁。为了克服这些困难，我们的项目专注于创建和验证一个先进的深度学习框架，能够处理复杂的视觉输入，精确、实时地识别各种环境中的车辆和行人。在一个复杂的都市数据集中，我们训练和评估了不同版本的YOLOv8和RT-DETR模型。YOLOv8大版本被证明是最有效的，特别是在行人识别方面，具有很高的精确度和鲁棒性。包括平均精度均值（Mean Average Precision，MAP）和召回率在内的结果表明，模型具有显著提高交通监测和安全的潜力。这项研究在实时、可靠的计算机视觉检测方面做出了重要的贡献，为交通管理系统树立了新的基准。
URL
https://arxiv.org/abs/2404.08081
