Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark

TL;DR

This paper introduces a normalized Wasserstein distance metric and a ranking-based assignment strategy to improve tiny object detection in aerial images, along with a new benchmark dataset AI-TOD-v2.

Contribution

It proposes NWD and RKA strategies for better label assignment in anchor-based detectors, significantly enhancing tiny object detection performance.

Findings

NWD-RKA improves detection accuracy on four datasets.

Embedding NWD-RKA into DetectoRS yields 4.3 AP points gain.

AI-TOD-v2 dataset reduces annotation noise and errors.

Abstract

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/