UAV Imagery for Automatic Multi-Element Recognition and Detection of Road Traffic Elements

Abstract

Road traffic elements comprise an important part of roads and represent the main content involved in the construction of a basic traffic geographic information database, which is particularly important for the development of basic traffic geographic information. However, the following problems still exist for the extraction of traffic elements: insufficient data, complex scenarios, small targets, and incomplete element information. Therefore, a set of road traffic multielement remote sensing image datasets obtained by unmanned aerial vehicles (UAVs) is produced, and an improved YOLOv4 network algorithm combined with an attention mechanism is proposed to automatically recognize and detect multiple elements of road traffic in UAV imagery. First, the scale range of different objects in the datasets is counted, and then the size of the candidate box is obtained by the k-means clustering method. Second, mosaic data augmentation technology is used to increase the number of trained road traffic multielement datasets. Then, by integrating the efficient channel attention (ECA) mechanism into the two effective feature layers extracted from the YOLOv4 backbone network and the upsampling results, the network focuses on the feature information and then trains the datasets. At the same time, the complete intersection over union (CIoU) loss function is used to consider the geometric relationship between the object and the test object, to solve the overlapping problem of the juxtaposed dense test element anchor boxes, and to reduce the rate of missed detection. Finally, the mean average precision (mAP) is calculated to evaluate the experimental effect. The experimental results show that the mAP value of the proposed method is 90.45%, which is 15.80% better than the average accuracy of the original YOLOv4 network. The average detection accuracy of zebra crossings, bus stations, and roadside parking spaces is improved by 12.52%, 22.82%, and 12.09%, respectively. The comparison experiments and ablation experiments proved that the proposed method can realize the automatic recognition and detection of multiple elements of road traffic, and provide a new solution for constructing a basic traffic geographic information database.