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Object Detection in Aerial Images: What Improves the Accuracy?

arXiv 2201.08763 ↗First posted 21 Jan 2022cs.CV cs.AIcited by 0

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Abstract (arXiv)

Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of object detection in aerial images. In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images. We conduct extensive experiments on the challenging iSAID dataset. The resulting adapted Faster R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline counterpart on the iSAID validation set, demonstrating the impact of different strategies investigated in this work.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 21 Jan 2022.

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26 of 37 references.

References · 26

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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14 more
  1. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 622
  2. 2017Soft-NMS -- Improving Object Detection With One Line of Codecited by 380
  3. 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 339
  4. 2018The Unmanned Aerial Vehicle Benchmark: Object Detection and Trackingcited by 207
  5. 2019iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Imagescited by 195
  6. 2018xView: Objects in Context in Overhead Imagerycited by 145
  7. 2018Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networkscited by 120
  8. 2021A Survey of Modern Deep Learning based Object Detection Modelscited by 97
  9. 2019Clustered Object Detection in Aerial Imagescited by 83
  10. 2021TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarioscited by 83
  11. 2021Adaptive Class Suppression Loss for Long-Tail Object Detectioncited by 55
  12. 2021Orthogonal Projection Losscited by 44
  13. 2021DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Imagescited by 7
  14. 2017Context-Aware Single-Shot Detectorcited by 4

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