Deep Learning-Based Vehicle Image Detection Using YOLOv5 With Region-Based Convolutional Neural Network

Abstract

The Deep Learning-Based Vehicle Image Detection system uses YOLOv5 integrated with a Region-Based Convolutional Neural Network (R-CNN) for accurate and real-time vehicle detection. The system identifies and classifies vehicles in images and video streams. It addresses challenges like varying vehicle sizes, angles, occlusion, and lighting conditions. YOLOv5 provides high-speed detection, while R-CNN improves localization accuracy. The hybrid approach combines speed and precision for intelligent traffic analysis. The model is trained on a diverse dataset of vehicle images. Detection results include bounding boxes and vehicle type labels. The system supports multiple vehicle classes such as cars, buses, trucks, and motorcycles. Real-time deployment is feasible on GPUs. It can be applied in traffic monitoring, smart parking, and autonomous driving. The system reduces manual traffic surveillance effort. Detection accuracy is validated using precision, recall, and F1-score. Visualizations include annotated images and video streams. Cloud integration ensures scalability. The system is robust to environmental variations. Automated alerts can be generated for traffic violations. Continuous learning updates improve performance. The approach demonstrates significant improvements over traditional methods. Overall, it provides an effective tool for intelligent transportation systems.