  Small object detection is challenging because small objects do not contain
detailed information and may even disappear in the deep network. Usually,
feeding high-resolution images into a network can alleviate this issue.
However, simply enlarging the resolution will cause more problems, such as
that, it aggravates the large variant of object scale and introduces unbearable
computation cost. To keep the benefits of high-resolution images without
bringing up new problems, we proposed the High-Resolution Detection Network
(HRDNet). HRDNet takes multiple resolution inputs using multi-depth backbones.
To fully take advantage of multiple features, we proposed Multi-Depth Image
Pyramid Network (MD-IPN) and Multi-Scale Feature Pyramid Network (MS-FPN) in
HRDNet. MD-IPN maintains multiple position information using multiple depth
backbones. Specifically, high-resolution input will be fed into a shallow
network to reserve more positional information and reducing the computational
cost while low-resolution input will be fed into a deep network to extract more
semantics. By extracting various features from high to low resolutions, the
MD-IPN is able to improve the performance of small object detection as well as
maintaining the performance of middle and large objects. MS-FPN is proposed to
align and fuse multi-scale feature groups generated by MD-IPN to reduce the
information imbalance between these multi-scale multi-level features. Extensive
experiments and ablation studies are conducted on the standard benchmark
dataset MS COCO2017, Pascal VOC2007/2012 and a typical small object dataset,
VisDrone 2019. Notably, our proposed HRDNet achieves the state-of-the-art on
these datasets and it performs better on small objects.
