Detecting The Small Object Recognition By Drone Images Using Yolo

Abstract

UAV imagery has become an essential tool in applications such as traffic monitoring, disaster response, and airspace management, owing to its flexibility, portability, and low operational cost. However, object detection in UAV images poses significant challenges due to factors like small object sizes, complex and cluttered backgrounds, and high levels of noise. To overcome these challenges, this study proposes an advanced object detection approach based on YOLOv10, a state-of-the-art model known for its enhanced architectural efficiency and detection capabilities. The model is optimized for UAV aerial scenarios, with a particular focus on improving small object detection through refined feature extraction and enhanced spatial understanding. The proposed YOLOv10-based framework integrates adaptive feature enhancement and deep semantic learning to improve detection performance under challenging UAV imaging conditions. By leveraging modern advancements in convolutional attention mechanisms, multi-scale detection heads, and optimized backbone architectures, the system effectively captures fine-grained details while maintaining real-time processing capabilities. This approach enables robust object detection in complex UAV environments and demonstrates the potential of YOLOv10 as a powerful solution for aerial imagery analysis.