MutDet: Mutually Optimizing Pre-training for Remote Sensing Object Detection

TL;DR

MutDet introduces a mutual enhancement and contrastive alignment framework for pre-training in remote sensing object detection, significantly improving transfer performance especially with limited data.

Contribution

The paper proposes a novel mutual enhancement module and contrastive alignment loss for pre-training, specifically addressing feature discrepancy issues in remote sensing detection tasks.

Findings

Achieves state-of-the-art transfer performance on remote sensing datasets.

Significantly improves detection accuracy with limited training data.

Demonstrates effectiveness of mutual enhancement and contrastive alignment in pre-training.

Abstract

Detection pre-training methods for the DETR series detector have been extensively studied in natural scenes, e.g., DETReg. However, the detection pre-training remains unexplored in remote sensing scenes. In existing pre-training methods, alignment between object embeddings extracted from a pre-trained backbone and detector features is significant. However, due to differences in feature extraction methods, a pronounced feature discrepancy still exists and hinders the pre-training performance. The remote sensing images with complex environments and more densely distributed objects exacerbate the discrepancy. In this work, we propose a novel Mutually optimizing pre-training framework for remote sensing object Detection, dubbed as MutDet. In MutDet, we propose a systemic solution against this challenge. Firstly, we propose a mutual enhancement module, which fuses the object embeddings and detector features bidirectionally in the last encoder layer, enhancing their information interaction.Secondly, contrastive alignment loss is employed to guide this alignment process softly and simultaneously enhances detector features' discriminativity. Finally, we design an auxiliary siamese head to mitigate the task gap arising from the introduction of enhancement module. Comprehensive experiments on various settings show new state-of-the-art transfer performance. The improvement is particularly pronounced when data quantity is limited. When using 10% of the DIOR-R data, MutDet improves DetReg by 6.1% in AP50. Codes and models are available at: https://github.com/floatingstarZ/MutDet.