# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 5 Conclusions  
This study is dedicated to addressing the ship detection problem in optical remote sensing images. The proposed ReBiDet, an enhanced version of ReDet, incorporates three improvements: ReBiFPN, DPRL sampler, and anchor improvement. These enhancements respectively bolster the network's ability to capture multi-scale features, learn difficult positive samples, and adapt to ship target aspect ratios. As a result, ReBiDet provides an effective and practically valuable solution for ship detection. The experimental results on HRSC2016 and DOTA datasets underscore the model's exceptional performance. Future research can concentrate on enhancing the model's performance by exploring more advanced network structures, loss functions, and optimization methods. Additionally, researchers can delve into additional optimization schemes for ship detection in remote sensing images to enhance accuracy and robustness. Moreover, it would be intriguing to explore the model's generalization capability on other datasets, such as FAIR1M [40]. **Author Contributions:** Methodology, Z.Y. ; Software, Z.Y. ; Supervision, Z.L. and Y.X. ; Writing--Original Draft, Z.Y. and S.L. ; Writing--Review and Editing, Z.Y., C.L. and Z.L. ;Visualization, F.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** The HRSC2016 dataset used in this study was publicly released by Northwestern Polytechnical University in 2016 and includes complete sets of training, validation, and testing images and annotation files. It can be downloaded at the following address: [[https://aistudio.baidu.com/aistudio/datasetdetail/54106](https://aistudio.baidu.com/aistudio/datasetdetail/54106)]([https://aistudio.baidu.com/aistudio/datasetdetail/54106](https://aistudio.baidu.com/aistudio/datasetdetail/54106)) (accessed on 11 June 2023). The DOTA dataset was publicly released by Wuhan University in November 2017 and includes complete sets of training, validation, and testing images, as well as annotation files for the training and validation sets. It can be downloaded at this address: [[https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)]([https://captain-whu.github.io/DOTA/dataset.html](https://captain-whu.github.io/DOTA/dataset.html)) (accessed on 11 June 2023). Since the downloaded package does not include annotation files for the testing set, users need to submit their model's inference results to the Wuhan University official Evaluation Server to obtain the accuracy of the inference results, at this address: [[https://captain-whu.github.io/DOTA/evaluation.html](https://captain-whu.github.io/DOTA/evaluation.html)]([https://captain-whu.github.io/DOTA/evaluation.html](https://captain-whu.github.io/DOTA/evaluation.html)) (accessed on 11 June 2023). Finally, we published some of the raw experiment results for this study at [[https://github.com/YanZeGit/ReBiDet](https://github.com/YanZeGit/ReBiDet)]([https://github.com/YanZeGit/ReBiDet](https://github.com/YanZeGit/ReBiDet)) (accessed on 11 June 2023). After this article is published, our code and other related materials will also be released at this address. **Acknowledgments:** This work was supported by the Institute of Systems Engineering, Academy of Military Sciences, and We would like to express our gratitude to [PERSON] and [PERSON], for their support and guidance throughout the project. Their contributions have been invaluable to the success of this study. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: