  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}.
