对比四类检测模型在卢旺达摩托车识别中的表现
Comparative Analysis of YOLOv5, Faster R-CNN, SSD, and RetinaNet for Motorbike Detection in Kigali Autonomous Driving Context
- 用自建198张图像数据集比较YOLOv5等四模型
- YOLOv5在精度与速度上综合表现最优
- 适合资源受限的非洲自动驾驶系统部署
在卢旺达基加利，摩托出租车是主要交通工具，常无序行驶且无视交通规则，给自动驾驶系统带来显著挑战。本研究基于在基加利采集的198张图像构建的定制数据集，对比了YOLOv5、Faster R-CNN、SSD和RetinaNet四种目标检测模型在摩托车检测中的表现。所有模型均使用PyTorch实现，并采用迁移学习。通过评估准确率、定位精度和推理速度，分析其在资源受限环境下的实时导航适用性。研究揭示了数据集局限性和模型复杂度带来的实施挑战，并建议未来工作采用简化架构以提升发展中国家如卢旺达自动驾驶系统的可及性。
原文摘要 · Abstract (English)
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.
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