The one-stage method is suitable for deployment on the terminal due to it only needing to be fed into the network once to predict all the bounding boxes, which is faster than the two-stage method. Many scholars focus on optimizing the speed of the one-stage SOTA algorithm to take advantage of its fast speed [23; 24; 25; 26; 27; 28; 29; 30]. For example, in 2020, the article [23] proposed a mixed YOLOv3-LTE detector, this method complements Residual Blocks (ResBlocks) and parallel high-to-low resolution sub-networks, makes full use of shallow network features while increasing network depth, and uses \"shallow and narrow\" convolutional layers to achieve lightweight characteristic; in 2022, the article [24] proposed a YOLOv4-tiny based lightweight object detection framework to improves inference speed without sacrificing accuracy than baseline method. In this article, channel attention, and spatial attention are used to extract more effective information and using ResBlock-D module replace the CSPDBlock module to reduces the computational complexity. Many scholars also focus on researching lightweight SAR image detection algorithms for high speed and low power consumption. In 2021, the article [25] proposed an efficient GPU par allel algorithm to accelerate image registration for InSAR image processing and achieved 10w power consumption on Nvidia Jetson. In 2021, article [26] proposed a lightweight detection framework that integrates CFAR and YOLOv4 to achieve on-orbit target detection of SAR ship images. The model size is 22 MB and achieves 85.9% precision on the HISEA-1 image set, but the detection framework is divided into two stages; the dependent soft environment is more complex, and it is still challenging to transplant for microsatellites. In 2022, the article [27] proposed a lightweight SAR ship detector named Lite-YOLOv5, with a model size of only 2.38 MB, and achieved 73.15% \(mAP\) in the LS-SSDD-v1.0 dataset. Still, this work does not assess the energy consumption of algorithm operation, which is a crucial metric for porting to satellite platforms. These earlier works explored the feasibility of the one-stage algorithm in the field of object detection in satellite remote sensing images and laid a technical foundation for the realization of the new paradigm of on-orbit interpretation. Inspired by the above work, this paper aims to study a more lightweight and high-precision target recognition algorithm, and qualitatively analyze and evaluate the training power consumption, so as to provide theoretical support for the goal of transplantation in the algorithm onboard. Based on the previous analysis, we mainly focus on studying the lightweight detection algorithm design to meet the task of detecting ships in orbit. We choose a one-stage lightweight detector, YOLOv5 [22], as the baseline method. Since YOLOv5 is a multiclass classifier, it has a scale optimization margin in a binary classification problem. Furthermore, some studies have shown that the vision transformer [31; 32] can improve performance in remote sensing tasks effectively [30]. Inspired by the vision transformer technology, we study the performance of the vision transformer encoder [31] connected at different locations in the lightweight detection framework. We propose a YOLO-based dedicated SAR ship detector: You Only Look Once with Vision Transformer Encoder for SAR Ship detection (YOLO-ViTSS); we test it on the SAR Ship Detection Dataset (SSDD) [33]. The results show that YOLO-ViTSS is lightweight, robust, and has low training energy consumption. The main contributions of this paper are as follows:  
1. This paper analyzes the state of the art of the novel on-orbit remote sensing interpretation paradigm architecture. Compared with traditional interpretation methods, The scheme proposed in this paper helps to build a novel real-time, eco-friendly and sustainable remote sensing image interpretation mode. 2. We study the fusion performance of the vision transformer module under the reduced YOLO framework.