We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5.
- Authors
Li, Shuaicai; Yang, Xiaodong; Lin, Xiaoxia; Zhang, Yanyi; Wu, Jiahui
- Abstract
Aerial vehicle detection has significant applications in aerial surveillance and traffic control. The pictures captured by the UAV are characterized by many tiny objects and vehicles obscuring each other, significantly increasing the detection challenge. In the research of detecting vehicles in aerial images, there is a widespread problem of missed and false detections. Therefore, we customize a model based on YOLOv5 to be more suitable for detecting vehicles in aerial images. Firstly, we add one additional prediction head to detect smaller-scale objects. Furthermore, to keep the original features involved in the training process of the model, we introduce a Bidirectional Feature Pyramid Network (BiFPN) to fuse the feature information from various scales. Lastly, Soft-NMS (soft non-maximum suppression) is employed as a prediction frame filtering method, alleviating the missed detection due to the close alignment of vehicles. The experimental findings on the self-made dataset in this research indicate that compared with YOLOv5s, the mAP@0.5 and mAP@0.5:0.95 of YOLOv5-VTO increase by 3.7% and 4.7%, respectively, and the two indexes of accuracy and recall are also improved.
- Subjects
TRAFFIC monitoring; AERIAL surveillance; AERIAL spraying &; dusting in agriculture; MINIATURE objects; TRAFFIC engineering; DRONE aircraft
- Publication
Sensors (14248220), 2023, Vol 23, Issue 12, p5634
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23125634