Aerial Object Detection: Deep learning approaches and applications

Aerial object detection is a revolutionary technique in deep learning and has seen increased adoption across various industries. It has transformed the way objects, such as vehicles, buildings, or natural features, are tracked and analyzed in aerial images or video streams. Unlike natural image object detection, aerial images provide a top-view perspective of geospatial objects, presenting unique challenges such as arbitrary orientations, scale variations, nonuniform object densities, and large aspect ratios.

This whitepaper explores the complexities of aerial object detection and proposes solutions to improve the accuracy and performance of aerial object detection models. The key topics covered in this whitepaper are:

  • Challenges in object detection for aerial images
  • Oriented object detection and its significance
  • Introduction to DOTA dataset, one of the largest in aerial object detection
  • Training state-of-the-art models with the DOTA dataset
  • Real-world applications such as surveillance, agriculture, traffic analysis, and more

About the Author

Dipayan Mukhopadhyay is an Associate Lead Data Scientist at Sigmoid. He has over 6 years of experience in Data Science, Statistical Modelling, Computer Vision, and NLP. With his extensive knowledge and experience in Data Science projects, he helps enterprises in Retail, CPG and Manufacturing extract meaningful insights from data to drive informed decision-making.

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