Low-Rank Adaption on Transformer-based Oriented Object Detector for Satellite Onboard Processing of Remote Sensing Images

TL;DR

This paper introduces a low-rank adaptation method for transformer-based satellite onboard object detection models, enabling efficient fine-tuning with minimal parameter updates while maintaining high detection performance.

Contribution

It proposes a parameter-efficient fine-tuning approach using LoRA for spaceborne object detection, reducing bandwidth needs and improving model adaptability.

Findings

Achieves 97-100% of full fine-tuning performance with only 12.4% of parameters updated.

Speeds up training and improves model robustness.

Reduces communication bandwidth for satellite onboard model updates.

Abstract

Deep learning models in satellite onboard enable real-time interpretation of remote sensing images, reducing the need for data transmission to the ground and conserving communication resources. As satellite numbers and observation frequencies increase, the demand for satellite onboard real-time image interpretation grows, highlighting the expanding importance and development of this technology. However, updating the extensive parameters of models deployed on the satellites for spaceborne object detection model is challenging due to the limitations of uplink bandwidth in wireless satellite communications. To address this issue, this paper proposes a method based on parameter-efficient fine-tuning technology with low-rank adaptation (LoRA) module. It involves training low-rank matrix parameters and integrating them with the original model's weight matrix through multiplication and summation, thereby fine-tuning the model parameters to adapt to new data distributions with minimal weight updates. The proposed method combines parameter-efficient fine-tuning with full fine-tuning in the parameter update strategy of the oriented object detection algorithm architecture. This strategy enables model performance improvements close to full fine-tuning effects with minimal parameter updates. In addition, low rank approximation is conducted to pick an optimal rank value for LoRA matrices. Extensive experiments verify the effectiveness of the proposed method. By fine-tuning and updating only 12.4$\%$ of the model's total parameters, it is able to achieve 97$\%$ to 100$\%$ of the performance of full fine-tuning models. Additionally, the reduced number of trainable parameters accelerates model training iterations and enhances the generalization and robustness of the oriented object detection model. The source code is available at: \url{https://github.com/fudanxu/LoRA-Det}.