TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios

Object detection on drone-captured scenarios is a recent popular task. On VisDrone Challenge 2021, TPH-YOLOv5 wins 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> place and achieves well-matched results with 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place model (AP 39.43%).

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• 1 As drones always navigate in different altitudes, the object scale varies violently, which burdens the optimization of networks.
• 2 Moreover, high-speed and low-altitude flight bring in the motion blur on the densely packed objects, which leads to great challenge of object distinction.
• 3 To solve the two issues mentioned above, we propose TPH-YOLOv5.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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