Research on Vehicle Detection Based on Faster R-CNN

While Faster R-CNN is a classical object detection framework widely used in vehicle detection, its performance tends to degrade when deployed in complex and dynamic traffic scenarios.This paper first introduces the fundamental principles of Faster R-CNN, providing a concise theoretical background.It then conducts an in-depth examination of its main limitations, such as limited adaptability to diverse environmental conditions, poor capability in detecting small or distant targets, insufficient real-time processing efficiency, and reduced accuracy when handling partially occluded vehicles.Building on recent advances in related research, several targeted improvement strategies are proposed.These include the adoption of domain adaptation techniques to enhance robustness across environments, network architecture optimization to improve detection precision, lightweight model design to strengthen real-time performance, and advanced strategies specifically tailored for occlusion-aware detection.By systematically analyzing Faster R-CNN's constraints and suggesting feasible solutions, this study aims to contribute both theoretical insights and practical guidance toward improving vehicle detection accuracy and reliability in increasingly complex traffic environments.