Deep Learning–Based Detection of Vehicle Axle Type with Images Collected via UAV

Abstract

The identification and quantification of vehicle axle type are essential to evaluate the operational status of road traffic. Uncrewed aerial vehicles (UAV) are helpful in obtaining information about vehicles in most road scenes. This paper proposed the collection of road vehicle information using UAVs with a high-resolution camera. The UAV flight scheme for optimal image quality acquisition was studied, and the collected UAV images were processed. An image data set was established with four vehicle types and nine vehicle axle types. Three state-of-the-art object-detection algorithm, namely, CenterNet, you only look once (YOLO)v7, and Detection Transformer (DTER), were used to train the data set, and their prediction performance was compared. YOLOv7 performed the best among the three algorithms with a mean average precision (MAP) of 97.1%. The YOLOv7 object-detection algorithm was combined with the DeepSORT object-tracking algorithm to achieve detection and statistics of vehicle axle type in traffic flow. The findings of this study help to quickly obtain basic information about the vehicles on the road.