The ambiguous appearance, tiny scale, and fine-grained classes of objects in remote sensing imagery inevitably lead to the noisy annotations in category labels of detection dataset. However, the effects and treatments of the label noises are underexplored in modern oriented remote sensing object detectors. To address this issue, we propose a robust oriented remote sensing object detection method through dynamic loss decay (DLD) mechanism, inspired by the two phase ``early-learning'' and ``memorization'' learning dynamics of deep neural networks on clean and noisy samples. To be specific, we first observe the end point of early learning phase termed as EL, after which the models begin to memorize the false labels that significantly degrade the detection accuracy. Secondly, under the guidance of the training indicator, the losses of each sample are ranked in descending order, and we adaptively decay the losses of the top K largest ones (bad samples) in the following epochs. Because these large losses are of high confidence to be calculated with wrong labels. Experimental results show that the method achieves excellent noise resistance performance tested on multiple public datasets such as HRSC2016 and DOTA-v1.0/v2.0 with synthetic category label noise. Our solution also has won the 2st place in the "fine-grained object detection based on sub-meter remote sensing imagery" track with noisy labels of 2023 National Big Data and Computing Intelligence Challenge.

翻译：遥感图像中目标的模糊外观、微小尺度及细粒度类别，不可避免地导致检测数据集中类别标签存在噪声标注。然而，现代有向遥感目标检测器对标签噪声影响及其应对策略的研究尚不充分。针对此问题，受深度神经网络在干净样本和噪声样本上呈现的"早期学习"与"记忆化"两阶段学习动力学现象启发，我们提出一种基于动态损失衰减（DLD）机制的鲁棒性有向遥感目标检测方法。具体而言：首先，我们观测到早期学习阶段（EL）的终止点，模型在该点之后开始记忆显著降低检测精度的错误标签；其次，在训练指标的引导下，将各样本的损失值降序排列，并在后续训练周期中自适应衰减前K个最大损失（即坏样本）的损失值——因为这些大损失值对应的样本极大概率是通过错误标签计算得出。实验结果表明，该方法在HRSC2016、DOTA-v1.0/v2.0等多个公开数据集上，针对合成类别标签噪声表现出优异的抗噪性能。本方案还在2023年全国大数据与计算智能挑战赛"亚米级遥感图像细粒度目标检测"赛道中，荣获噪声标签赛项第二名。