Real-Time Traffic Density Estimation with ROIAware DNN and Advanced YOLOv4-SSD Framework

Abstract

This research proposes real-time traffic density estimation based on Deep Neural Networks (DNNs) considering Region of Interest (ROI) estimation and object detection precision. A CNN-based ROI processing strategy is used to focus vehicle detection on relevant road regions and reduce unnecessary background processing. The proposed model is a blend of YOLOv4 and enhanced Single Shot Detector (SSD) for accuracy and speed in various conditions such as occlusion and varying ambient light. ROI masking restricts vehicle detection to the relevant road region, thereby improving computational efficiency and detection consistency. Algorithms and equations formulate the process of bounding box retrieval, traffic density computation, and confidence ranking. Figures and flowcharts present the design and step-by-step methodology of ROI estimation and vehicle detection. Real-time video frames are overlaid, processed, and preprocessed with density outputs and bounding boxes. Dynamic vehicle classification is provided by YOLOv4-SSD fusion within ROIs and estimates traffic density. The method supports real-time traffic surveillance and provides a scalable framework for traffic-density monitoring in urban environments. Results of CNN, RCNN, SSD, YOLO, and YOLOv4-SSD are compared in terms of Accuracy, Precision, Recall, and F-measure. YOLOv4 with Enhanced SSD was the best-performing among all with 99.05% accuracy and 98.38% F-measure, proving its credibility. Graphical comparison attests to its outperformance over object detection tasks. The model is also found to be suitable for real-time traffic monitoring because of its speed, precision, and versatility.