  Tiny object detection (TOD) in aerial images is challenging since a tiny
object only contains a few pixels. State-of-the-art object detectors do not
provide satisfactory results on tiny objects due to the lack of supervision
from discriminative features. Our key observation is that the Intersection over
Union (IoU) metric and its extensions are very sensitive to the location
deviation of the tiny objects, which drastically deteriorates the quality of
label assignment when used in anchor-based detectors. To tackle this problem,
we propose a new evaluation metric dubbed Normalized Wasserstein Distance (NWD)
and a new RanKing-based Assigning (RKA) strategy for tiny object detection. The
proposed NWD-RKA strategy can be easily embedded into all kinds of anchor-based
detectors to replace the standard IoU threshold-based one, significantly
improving label assignment and providing sufficient supervision information for
network training. Tested on four datasets, NWD-RKA can consistently improve
tiny object detection performance by a large margin. Besides, observing
prominent noisy labels in the Tiny Object Detection in Aerial Images (AI-TOD)
dataset, we are motivated to meticulously relabel it and release AI-TOD-v2 and
its corresponding benchmark. In AI-TOD-v2, the missing annotation and location
error problems are considerably mitigated, facilitating more reliable training
and validation processes. Embedding NWD-RKA into DetectoRS, the detection
performance achieves 4.3 AP points improvement over state-of-the-art
competitors on AI-TOD-v2. Datasets, codes, and more visualizations are
available at: https://chasel-tsui.github.io/AI-TOD-v2/
