Comparison and summary of Faster R-CNN, YOLOv3 and YOLOv5 applied in vehicle detection
Sirui Luo · Journal of Physics Conference Series · 2023
Abstract Nowadays, computer vision and machine learning are widely applied in vehicle detection. The technique of object detection is also of necessity in this realm. However, due to the large variety of models in object detection, it is important to find the most specifically suitable model for detecting vehicles in many situations. In this paper, we focus on comparing and summarizing Faster R-CNN, YOLOv3 and YOLOv5 applied in vehicle detection. We would introduce the models in relative detail and design an experiment to verify the models’ performances. The methodology for the experiment is to train the three models using the same dataset of vehicles, compare the different attributes of these results and find the most suitable application scenario for vehicle detection for each model.