Abstract:

In the context of smart cities and autonomous systems, real-time traffic surveillance and detection are essential for efficient urban mobility and safety. This paper introduces an innovative framework that integrates deep learning and computer vision techniques to enhance traffic monitoring systems. By combining convolutional neural networks (CNNs) with advanced object detection algorithms like YOLOv5 and Faster R-CNN, the system achieves high accuracy and speed in detecting and classifying various traffic elements, including vehicles, pedestrians, and traffic signals.The system processes video feeds from roadside cameras in real-time, providing actionable insights and alerts. To handle diverse environmental conditions and varying traffic scenarios, the approach incorporates data augmentation, transfer learning, and real-time optimization. The results demonstrate significant improvements in detection accuracy and processing speed compared to traditional methods, highlighting the potential for deploying these techniques in real-world traffic management systems.

Description:

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