How to Detect Small Objects in Drone Imagery

The tutorial by Mostafa Ibrahim outlines a method for detecting small objects in drone imagery using the RF-DETR model, trained on a dataset of 7,000 aerial images with over 120,000 annotations. The process involves using high training resolution, inference-time slicing, and careful annotation to improve detection accuracy for small objects like people and vehicles, which are often only a few pixels in size. The tutorial highlights the challenges of aerial detection, such as crowded scenes and motion blur, and demonstrates deploying the model in a Roboflow Workflow that integrates object detection, Python-based counting, and Gemini scene inspection. The workflow allows for the visualization and labeling of detected objects, generation of numerical summaries, and a concise visual inspection of the scene. The guide emphasizes the importance of model architecture, annotation quality, and the use of techniques like the SAHI method to enhance detection capabilities, especially in complex aerial environments.

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