Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey

By Xin Wu, Wei Li, Danfeng Hong, Ran Tao, Qian Du. IEEE Geoscience and Remote Sensing Magazine, 2021

296 citations recorded by the source corpus.

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by the recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, and traffic management.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c41be17073461f9ad25ade073fef8789c46382e8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Xin Wu
2. Jocelyn Chanussot
3. Danfeng Hong

In Computer Vision and Pattern Recognition

1. Jocelyn Chanussot
2. Danfeng Hong
3. Xin Wu
4. Shutao Li
5. Leyuan Fang
6. Antonio J. Plaza

Citations in the corpus

• UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, background): “INFRARED sensors [1] are widely used in civil and military applications [2] since it is insensitive to the environment, illumination, occlusion, and other conditions.”