Comparative Analysis of YOLOv5, Faster R-CNN, SSD, and RetinaNet for Motorbike Detection in Kigali Autonomous Driving Context
It addresses the challenge of detecting unpredictable motorbikes for autonomous driving systems in resource-constrained settings like Rwanda, but it is incremental as it applies existing methods to a new dataset.
This study compared four object detection models (YOLOv5, Faster R-CNN, SSD, and RetinaNet) for detecting motorbikes in Kigali, Rwanda, using a custom dataset of 198 images to evaluate their accuracy, localization, and inference speed for autonomous driving applications.
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.