Dynamic Loss Decay based Robust Oriented Object Detection on Remote Sensing Images with Noisy Labels

TL;DR

This paper introduces a dynamic loss decay method to improve robustness of oriented object detection in remote sensing images with noisy labels, inspired by neural network learning dynamics, and demonstrates its effectiveness on multiple datasets.

Contribution

The paper proposes a novel dynamic loss decay mechanism that adaptively reduces the influence of noisy labels during training, enhancing detection accuracy in noisy remote sensing datasets.

Findings

Achieves high noise resistance on multiple datasets.

Wins 2nd place in a major remote sensing detection challenge.

Effectively suppresses false label influence during training.

Abstract

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.