# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 4 Discussion  
The experimental results validate the effectiveness of our proposed ReBiDet model in ship detection tasks, indicating the efficacy of our designed methods and modules. ReBiFPN efficiently performs cross-fusion between deep features, which possess large receptive fields and strong semantic representation ability, and shallow features that lack spatial geometric feature details and have low resolution. This approach exhibits significant performance advantages compared to traditional feature pyramid networks. Each layer acquires more comprehensive and rich feature information, thereby facilitating downstream task execution and considerably improving the model's detection ability. The anchor improvement method aggregates features from annotated boxes in the dataset, adjusting the parameters of the anchor generator accordingly, thereby enhancing the model's detection capability for specific objects. The DPRL sampler module enables the model to focus on learning difficult positive samples, while avoiding the interference of false difficult negative samples. In summary, these three proposed methods or modules hold value in improving the ship detection capability in optical remote sensing images. Nevertheless, it is important to acknowledge that any method or research inherently possesses certain limitations. Our research primarily falls within the qualitative category, and there is still ample room for exploration in the quantitative aspects, which represents our future direction. The ReBiFPN module, inspired by PANet [29], indeed yielded positive results. The EfficientDet research [57] demonstrates that stacking multiple bi-directional feature fusion paths further enhances the detection accuracy on the COCO dataset [58]. Moreover, the detection accuracy varies with the number of stacked layers. Hence, in  
Figure 14: Examples of detection results on the DOTA dataset using ReBiDet. our field of optical remote sensing detection, it is crucial to investigate whether a similar effect exists, which will be the focus of our forthcoming quantitative research. Regarding the anchor improvement method, we have only roughly aggregated the features of ships in the existing dataset and adjusted the parameters of the anchor generator, achieving a qualitative effect. To put it figuratively, this can be likened to \"casting a wide net to catch more fish\" (a common idiom). Additionally, the quantitative research lies in how to set the anchor generator reasonably and accurately to strike a balance between accuracy and computational complexity. After introducing the OHEM sampler module, we observed a decrease in detection accuracy instead of an increase. Through repeated experiments and in-depth analysis, we proposed the DPRL sampler module to enhance the learning of difficult positive samples, thereby beneficially improving the model's detection accuracy. As for low-loss positive samples, it raises the question of whether it is the correct choice to abandon direct learning. In terms of negative samples, ships that are only partially anchored and interference from land structures, roads, and vegetation are considered difficult negative samples. Although random sampling is a feasible approach, it remains uncertain whether more reasonable methods exist to address these challenges. We firmly believe that such methods do indeed exist. For ship detection in optical remote sensing images, our research is still in its early stages, with quantitative research serving as the focal point of our future work. Concerning the issue of inaccurate detection accuracy evaluation due to incomplete object annotations in HRSC2016, we intend to manually re-annotate all ship targets in the HRSC2016 dataset in our next study. This will enable us to conduct a comprehensive comparison of various evaluation indicators based on the model's performance in existing detection models, ultimately leading to breakthroughs in improving detection performance.