Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images
Zhongkai University of Agriculture and Engineering
Abstract
Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. In this article, we propose Drone-YOLO, a series of multi-scale UAV image object detection algorithms based on the YOLOv8 model, designed to overcome the specific challenges associated with UAV image object detection. To address the issues of large scene sizes and small detection objects, we introduce improvements to the neck component of the YOLOv8 model. Specifically, we…
Citation impact
- FWCI
- 29.64
- Percentile
- 100%
- References
- 34
Authors
1Topics & keywords
- Drone
- Computer science
- Artificial intelligence
- Object detection
- Aerial image
- Computer vision
- Upsampling
- Convolutional neural network