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Dataset for small object detection with shadow (SODwS).

Shahbe Mat-Desa1, Wan-Noorshahida Mohd-Isa1, Petra Gomez-Krämer2

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A new dataset aids small object detection in aerial images, addressing challenges like shadows and limited data. This resource supports improved model training and future research in computer vision.

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Image Analysis

Background:

  • Detecting small objects in aerial imagery is difficult due to low resolution, scale variation, clutter, and occlusion.
  • Existing annotated datasets for small objects in aerial images are scarce, hindering model development.
  • Shadows significantly impact object visibility and detection accuracy in aerial images.

Purpose of the Study:

  • Introduce a novel dataset for small object detection in low-altitude aerial images.
  • Address the specific challenges posed by shadows obscuring small objects.
  • Provide a valuable resource for training and validating detection models and facilitating transfer learning.

Main Methods:

  • Curated a dataset of low-altitude aerial images featuring small objects.
  • Included images with small objects obscured by shadows to simulate real-world conditions.
  • Generated ground-truth shadow maps for each image to support shadow detection research.

Main Results:

  • The dataset contains diverse examples of small objects under various conditions, including shadow occlusion.
  • Includes precise annotations for small objects and corresponding shadow maps.
  • The dataset is suitable for training robust small object detection models.

Conclusions:

  • The new dataset effectively addresses the scarcity of annotated data for small object detection in aerial imagery.
  • It provides a unique resource for studying the impact of shadows on detection and developing shadow-aware models.
  • This dataset will advance research in aerial image analysis and object detection.