We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
YOLOv5-Based Dense Small Target Detection Algorithm for Aerial Images Using DIOU-NMS.
- Authors
Yu WANG; Xiang ZOU; Jiantong SHI; Minhua LIU
- Abstract
With the advancement of various aerial platforms, there is an increasing abundance of aerial images captured in various environments. However, the detection of densely packed small objects within complex backgrounds remains a challenge. To address the task of detecting multiple small objects, a multi-object detection algorithm based on Distance Intersection Over Union loss Nonminimum Suppression (DIOU-NMS) integrated with You Only Look Once version 5 (YOLOv5) is proposed. Leveraging the YOLOv5s model as the foundation, the algorithm specifically addresses the detection of abundantly and densely packed targets by incorporating a dedicated small object detection layer within the network architecture, thus effectively enhancing the detection capability for small targets using an additional up sampling operation. Moreover, conventional non-maximum suppression is replaced with DIOU-based non-maximum suppression to alleviate the issue of missed detections caused by target density. Experimental results demonstrate the effectiveness of the proposed method in significantly improving the detection performance of dense small targets in complex backgrounds.
- Subjects
OBJECT recognition (Computer vision); ALGORITHMS; ELEVATING platforms; TRACKING algorithms
- Publication
Radioengineering, 2024, Vol 33, Issue 1, p12
- ISSN
1210-2512
- Publication type
Article
- DOI
10.13164/re.2024.0012