Fast Region-based Convolutional Neural Network in Object Detection: A Review

Abstract

The evolution of Faster R-CNN within the field of among the most important tasks is object detection impactful developments in modern computer vision, offering high accuracy and architectural flexibility across a range of visual recognition tasks. This review presents a systematic analysis of how Faster R-CNN has been adapted and optimized between 2020 and 2025, examining its applications across diverse domains. The paper investigates three primary challenges where Faster R-CNN has shown considerable advancement: handling occluded objects, improving small object detection, and adapting to real-time constraints through lightweight and context-aware architectures. The analysis reveals notable performance improvements resulting from these enhancements: feature fusion and attention modules have improved detection in occluded scenes, small object detection has benefited from multi-scale representation and loss function refinement, and lightweight adaptations have expanded its usability in constrained environments. Collectively, these developments demonstrate how Faster R-CNN continues to evolve as a robust backbone for detection tasks. Nevertheless, challenges remain, including computational complexity, inference latency, and data dependency. By critically assessing recent advancements and ongoing limitations, this review offers comprehensive insights into the current state and future directions of Faster R-CNN-based object detection systems.