# Comparative Analysis of YOLOv5, Faster R-CNN, SSD, and RetinaNet for Motorbike Detection in Kigali Autonomous Driving Context

arXiv: https://arxiv.org/abs/2510.04912 · HTML: https://mlsanity.com/paper/2510.04912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [FSSD: Feature Fusion Single Shot Multibox Detector](https://mlsanity.com/paper/1712.00960/) (2017)
- [Efficient Feature Fusion for UAV Object Detection](https://mlsanity.com/paper/2501.17983/) (2025)
- [DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes](https://mlsanity.com/paper/2507.19912/) (2025)
- [Data Augmentation For Small Object using Fast AutoAugment](https://mlsanity.com/paper/2506.08956/) (2025)

## Built on by (most cited first, 2 of 2)

- [Building UI/UX Dataset for Dark Pattern Detection and YOLOv12x-based Real-Time Object Recognition Detection System](https://mlsanity.com/paper/2512.18269/) (2025)
- [One-Stage Object Detectors in Autonomous Driving](https://mlsanity.com/paper/2608.19014/) (2026)

## Abstract (arXiv)

In Kigali, Rwanda, motorcycle taxis are a primary mode of transportation, often navigating unpredictably and disregarding traffic rules, posing significant challenges for autonomous driving systems. This study compares four object detection models--YOLOv5, Faster R-CNN, SSD, and RetinaNet--for motorbike detection using a custom dataset of 198 images collected in Kigali. Implemented in PyTorch with transfer learning, the models were evaluated for accuracy, localization, and inference speed to assess their suitability for real-time navigation in resource-constrained settings. We identify implementation challenges, including dataset limitations and model complexities, and recommend simplified architectures for future work to enhance accessibility for autonomous systems in developing countries like Rwanda.