UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective

By Mingjie Liu, Xianhao Wang, Anjian Zhou, Xiuyuan Fu, Yiwei Ma, Chang Hao Piao. Italian National Conference on Sensors, 2020

311 citations recorded by the source corpus.

Object detection, as a fundamental task in computer vision, has been developed enormously, but is still challenging work, especially for Unmanned Aerial Vehicle (UAV) perspective due to small scale of the target. In this study, the authors develop a special detection method for small objects in UAV perspective. Based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height. Then, the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information. Both these two optimizations can enlarge the receptive filed. Furthermore, UAV-viewed dataset is collected to UAV perspective or small object detection. An optimized training method is also proposed based on collected UAV-viewed dataset. The experimental results on public dataset and our collected UAV-viewed dataset show distinct performance improvement on small object detection with keeping the same level performance on normal dataset, which means our proposed method adapts to different kinds of conditions.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a00ec3d139a444455bb82ac9b6b80517f08802dd

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. Qian Du
2. Xin Wu
3. G. Wang
4. Danfeng Hong
5. Wei Li

In Computer Vision and Pattern Recognition

1. Qian Du
2. Danfeng Hong
3. Wei Li
4. Xin Wu
5. Ran Tao
6. Hanyu Hong
7. Pei An
8. G. Wang

Citations in the corpus

• UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios (2023, methodology): “[23] proposed a small-target-detection method in the UAV view to reduce the leakage and false detection rate of small targets.”
• Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A survey (2021, background): “[221] P. Adarsh, P. Rathi, and M. Kumar, “YOLO v3-tiny: Object detection and recognition using one stage improved model,” in Proc.”