YOLO-Fine: One-Stage Detector of Small Objects under Various Backgrounds in Remote Sensing Images
Object detection from aerial and satellite remote sensing images has been an active research topic over the past decade. Thanks to the increase in computational resources and data availability, deep learning-based object detection methods have achieved numerous successes in computer vision, and more...
Main Authors: | Minh-Tan Pham, Luc Courtrai, Chloé Friguet, Sébastien Lefèvre, Alexandre Baussard |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-08-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/12/15/2501 |
Similar Items
-
The One-Stage Detector Algorithm Based on Background Prediction and Group Normalization for Vehicle Detection
by: Fei Lu, et al.
Published: (2020-08-01) -
Design and performance of kinetic inductance detectors for cosmic microwave background polarimetry
by: McCarrick, Heather
Published: (2018) -
SSD vs. YOLO for Detection of Outdoor Urban Advertising Panels under Multiple Variabilities
by: Ángel Morera, et al.
Published: (2020-08-01) -
Fast-neutron heterogeneous scintillation detector with high discrimination of gamma background
by: Yuri I. Chernukhin, et al.
Published: (2015-10-01) -
Low Background Radiation Detection Techniques and Mitigation of Radioactive Backgrounds
by: Matthias Laubenstein, et al.
Published: (2020-11-01)