  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.
