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.

远程感知图像中微小目标的精确检测仍然是一个重要的挑战，因为这些目标的视觉信息有限且在场景中经常出现。本研究针对噪声标签监督下的微小目标检测问题进行了系统研究，并提出了一种去噪微小目标检测器（DN-TOD），其中包括了类别感知标签校验和趋势引导学习策略来应对类别偏移和边界框噪声问题。DN-TOD 在各种类型的标签噪声下表现出了鲁棒性，显著改进了基准模型 RFLA 在 40% 混合噪声下的性能。

高噪声环境下航空图像中的稳健微小目标检测

Robust Tiny Object Detection in Aerial Images amidst Label Noise

Training deep object detectors requires significant amount of human-annotated
images with accurate object labels and bounding box coordinates, which are
extremely expensive to acquire. Noisy annotations are much more easily
accessible, but they could be detrimental for learning. We address the
challenging problem of training object detectors with noisy annotations, where
the noise contains a mixture of label noise and bounding box noise. We propose
a learning framework which jointly optimizes object labels, bounding box
coordinates, and model parameters by performing alternating noise correction
and model training. To disentangle label noise and bounding box noise, we
propose a two-step noise correction method. The first step performs
class-agnostic bounding box correction by minimizing classifier discrepancy and
maximizing region objectness. The second step distils knowledge from dual
detection heads for soft label correction and class-specific bounding box
refinement. We conduct experiments on PASCAL VOC and MS-COCO dataset with both
synthetic noise and machine-generated noise. Our method achieves
state-of-the-art performance by effectively cleaning both label noise and
bounding box noise. Code to reproduce all results will be released.

本文提出了一种训练目标检测器的框架，该框架可以处理含有混合标签噪声和边界框噪声的嘈杂标注数据，并通过交替噪声校正和模型训练来共同优化目标标签、边界框坐标和模型参数，其方法通过执行两个步骤来分离标签噪声和边界框噪声，并在 PASCAL VOC 和 MS-COCO 数据集上获得了良好的实验效果。