  Object detection on drone-captured scenarios is a recent popular task. As
drones always navigate in different altitudes, the object scale varies
violently, which burdens the optimization of networks. 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. To solve the two issues
mentioned above, we propose TPH-YOLOv5. Based on YOLOv5, we add one more
prediction head to detect different-scale objects. Then we replace the original
prediction heads with Transformer Prediction Heads (TPH) to explore the
prediction potential with self-attention mechanism. We also integrate
convolutional block attention model (CBAM) to find attention region on
scenarios with dense objects. To achieve more improvement of our proposed
TPH-YOLOv5, we provide bags of useful strategies such as data augmentation,
multiscale testing, multi-model integration and utilizing extra classifier.
Extensive experiments on dataset VisDrone2021 show that TPH-YOLOv5 have good
performance with impressive interpretability on drone-captured scenarios. On
DET-test-challenge dataset, the AP result of TPH-YOLOv5 are 39.18%, which is
better than previous SOTA method (DPNetV3) by 1.81%. On VisDrone Challenge
2021, TPHYOLOv5 wins 5th place and achieves well-matched results with 1st place
model (AP 39.43%). Compared to baseline model (YOLOv5), TPH-YOLOv5 improves
about 7%, which is encouraging and competitive.
