Research on Vehicle Detection Based on Faster R-CNN

DOI

10.2991/978-94-6463-986-5_69How to use a DOI?

Keywords

Faster R-CNN; Vehicle Detection; Environmental Conditions; Real-Time Performance

Abstract

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yuhao Zhao
PY  - 2026
DA  - 2026/02/18
TI  - Research on Vehicle Detection Based on Faster R-CNN
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 671
EP  - 679
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_69
DO  - 10.2991/978-94-6463-986-5_69
ID  - Zhao2026
ER  -