Search Results for author:
Found 5 papers, 3 papers with code
Fewer is More: Efficient Object Detection in Large Aerial Images
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not.
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively.
Anchor-free Oriented Proposal Generator for Object Detection
Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them.
Oriented R-CNN for Object Detection
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.
Ranked #10 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers.
