Abstract
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.
Abstract (translated)
在卢旺达的基加利，摩托车出租车是主要的交通工具之一，它们常常不遵守交通规则且行驶路径难以预测，这对自动驾驶系统构成了重大挑战。这项研究比较了四种物体检测模型——YOLOv5、Faster R-CNN、SSD 和 RetinaNet，在使用从基加利收集的包含198张图片的自定义数据集的情况下，用于摩托车检测的效果。这些模型在 PyTorch 中实现，并采用了迁移学习技术，根据准确性、定位能力和推理速度对它们进行了评估，以确定它们在资源受限环境中的实时导航适用性。我们识别了实施过程中的挑战，包括数据集限制和模型复杂度问题，并建议在未来的工作中采用简化架构，以便于在像卢旺达这样的发展中国家提高自动驾驶系统的可访问性。
URL
https://arxiv.org/abs/2510.04912
