Robust Tiny Object Detection in Aerial Images amidst Label Noise
Haoran Zhu Chang Xu Wen Yang Ruixiang Zhang Yan Zhang Gui-Song Xia
Precise detection of tiny objects in remote sensing imagery remains a
significant challenge due to their limited visual information and frequent
occurrence within scenes. This challenge is further exacerbated by the
practical burden and inherent errors associated with manual annotation:
annotating tiny objects is laborious and prone to errors (i.e., label noise).
Training detectors for such objects using noisy labels often leads to
suboptimal performance, with networks tending to overfit on noisy labels. In
this study, we address the intricate issue of tiny object detection under noisy
label supervision. We systematically investigate the impact of various types of
noise on network training, revealing the vulnerability of object detectors to
class shifts and inaccurate bounding boxes for tiny objects. To mitigate these
challenges, we propose a DeNoising Tiny Object Detector (DN-TOD), which
incorporates a Class-aware Label Correction (CLC) scheme to address class
shifts and a Trend-guided Learning Strategy (TLS) to handle bounding box noise.
CLC mitigates inaccurate class supervision by identifying and filtering out
class-shifted positive samples, while TLS reduces noisy box-induced erroneous
supervision through sample reweighting and bounding box regeneration.
Additionally, Our method can be seamlessly integrated into both one-stage and
two-stage object detection pipelines. Comprehensive experiments conducted on
synthetic (i.e., noisy AI-TOD-v2.0 and DOTA-v2.0) and real-world (i.e., AI-TOD)
noisy datasets demonstrate the robustness of DN-TOD under various types of
label noise. Notably, when applied to the strong baseline RFLA, DN-TOD exhibits
a noteworthy performance improvement of 4.9 points under 40% mixed noise.
Datasets, codes, and models will be made publicly available.