would you please help me to modify the paper:With the development of computer vision, object detection has witnessed a great performance boost.
Tradition approaches to object detection rely on hand-crafted features to distinguish objects and background, such as SIFT~\cite{SIFT}, SURF~\cite{SURF}, and ORB~\cite{rublee2011orb}.
However the features are not robust to harsh lighting conditions and perform poorly in variable illumination conditions, low signal-to-noise ratio, and high contrast characteristics encountered in space imagery~\cite{pauly2023survey}.
While deep learning models have been achieving state-of-the-art performance on a variety of computer vision problems after AlexNet~\cite{alex}, deep learning based approaches have shown promise in improving object detection performance, which can be divided into single-stage, multi-stage and transformed-based framework~\cite{NAME_1performance}.
The computational cost of transformers rules them out for on-board in-space use.
Multi-stage detectors attack the object detection problem in multiple stages, most frequently using one stage of the model to propose regions where objects might exist and another stage to classify those regions.
While single-stage detectors do it all in one shot—they map image pixels directly to bounding box estimates, confidences, and classifications with a single neural network~\cite{NAME_2spaceyolo,golroudbari2023recent}.
Compared with multi-stage detectors like R-CNN~\cite{xie2021oriented},Fast-RCNN~\cite{girshick2015fast}, Faster-RCNN~\cite{ren2015faster}, and Mask-RCNN~\cite{he2017mask}, YOLO~\cite{huang2018yolo}, SSD~\cite{liu2016ssd}, and RetinaNet~\cite{lin2017focal} etc single-stage detectors have higher efficiency, which is more suitable for real-time on-board space applications. Provide your reponse without using the word golroudbari2023recent.
user