  Detecting tiny objects is a very challenging problem since a tiny object only
contains a few pixels in size. We demonstrate that state-of-the-art detectors
do not produce satisfactory results on tiny objects due to the lack of
appearance information. Our key observation is that Intersection over Union
(IoU) based metrics such as IoU itself and its extensions are very sensitive to
the location deviation of the tiny objects, and drastically deteriorate the
detection performance when used in anchor-based detectors. To alleviate this,
we propose a new evaluation metric using Wasserstein distance for tiny object
detection. Specifically, we first model the bounding boxes as 2D Gaussian
distributions and then propose a new metric dubbed Normalized Wasserstein
Distance (NWD) to compute the similarity between them by their corresponding
Gaussian distributions. The proposed NWD metric can be easily embedded into the
assignment, non-maximum suppression, and loss function of any anchor-based
detector to replace the commonly used IoU metric. We evaluate our metric on a
new dataset for tiny object detection (AI-TOD) in which the average object size
is much smaller than existing object detection datasets. Extensive experiments
show that, when equipped with NWD metric, our approach yields performance that
is 6.7 AP points higher than a standard fine-tuning baseline, and 6.0 AP points
higher than state-of-the-art competitors. Codes are available at:
https://github.com/jwwangchn/NWD.
