Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

Abstract

In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird's-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper,we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images.

Accepted to IEEE TPAMI

References in corpus (3)

• MMDetection: Open MMLab Detection Toolbox and Benchmark
• Light-Head R-CNN: In Defense of Two-Stage Object Detector
• R${}^2$-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images

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• Backdoor Attacks for Remote Sensing Data with Wavelet Transform
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• Oriented RepPoints for Aerial Object Detection
• Sampling Equivariant Self-attention Networks for Object Detection in Aerial Images