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

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

• By Sastra, Ricki; Hariyanto, Dicky; Apriyansyah, Bahalwan;
• Jurnal
• 2407
• 34-40

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.

• Katakunci Faster R-CNN, Object Detection, Occluded Objects, CNN, Computer vision
• URL https://journal.cendekiajournal.com/ijaci/article/view/18
• Penerbit -, - -

REFERENSI

[1] S. Zhai, S. Dong, D. Shang, and S. Wang, “An Improved Faster R-CNN Pedestrian Detection Algorithm Based on
Feature Fusion and Context Analysis,” IEEE Access, vol. 8, pp. 138117–138128, 2020, doi:
10.1109/ACCESS.2020.3012558.
[2] H. Yuan, Y. Shao, Z. Liu, and H. Wang, “An Improved Faster R-CNN for Pulmonary Embolism Detection from
CTPA Images,” IEEE Access, vol. 9, pp. 105382–105392, 2021, doi: 10.1109/ACCESS.2021.3099479.
[3] S. L. Chen et al., “Detection of Various Dental Conditions on Dental Panoramic Radiography Using Faster R-CNN,”
IEEE Access, vol. 11, no. October, pp. 127388–127401, 2023, doi: 10.1109/ACCESS.2023.3332269.
[4] Z. Guo et al., “Martian Dust Devil Detection Based on Improved Faster R-CNN,” IEEE J. Sel. Top. Appl. Earth
Obs. Remote Sens., vol. 17, pp. 7725–7737, 2024, doi: 10.1109/JSTARS.2024.3367848.
[5] B. Weihong, J. Yun, L. Jiaxin, S. Lingling, F. Guangwei, and J. Wa, “In-Situ Detection Method of Jellyfish Based
on Improved Faster R-CNN and FP16,” IEEE Access, vol. 11, no. July, pp. 81803–81814, 2023, doi:
10.1109/ACCESS.2023.3300655