# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 1 Introduction
### Goal of the Research  
Although the methods mentioned above demonstrated good performance in ship detection, they still encounter several challenges. This study primarily focuses on two key issues:  
(1) High-resolution optical remote sensing images contain rich pixel information of ships and other objects, but they also introduce a considerable amount of irrelevant, redundant, and potentially interfering noise. In specific scenarios, such as variations in lighting conditions and the limitations of satellite-borne sensors, the color contrast between ships and the background in some images may be low, leading to visual similarity to the human eye. Moreover, in harbor images, the shape of the dock can resemble that of large ships moored nearby. These factors contribute to the potential confusion of ships with complex backgrounds by detection models, resulting in missed detections and false alarms.  
(2) Ships are typically sparsely and unevenly distributed in optical remote sensing images. Inappropriate sampling strategies may lead to the insufficient learning of ship features by the detection model. For example, the random sampling strategy commonly employed by many models, including ReDet, may exclude certain potential high-quality positive samples from the training process.  
The ReDet model fails to address the aforementioned challenges. The upsampling operation in its FPN module may lead to the loss of crucial information, resulting in incomplete feature fusion and ultimately affecting the accuracy of the detection results. Additionally, the random sampler used in the RPN stage may lead to a small number of certain classes or samples due to class imbalance or sample imbalance, impacting the effectiveness of model training.  
The objective of this study is to propose a solution based on ReDet to tackle the aforementioned problems, specifically focusing on ship object detection in remote sensing images. We introduce the ReBiDet model, which encompasses the following contributions: Firstly, we design a feature pyramid module called ReBiFPN based on rotational equivariance theory. ReBiFPN ensures balanced output levels and replaces the FPN module in the ReDet model. By integrating high-resolution features from higher layers with detailed information from lower layers, it enhances the detection performance of ships at multiple scales. Secondly, we propose the positive reinforcement learning sampler and an anchor optimization module based on K-means clustering. These components are utilized to balance the difficulty and proportion of positive samples during the training process, as well as optimizing the sizes and aspect ratios of anchors, further enhancing the model's detection performance. Thirdly, we validate and compare the generalization performance of ReBiDet by selecting images from different optical remote sensing datasets. Finally, following the practices of other researchers in the field of optical remote sensing image object detection, we conduct experiments on the HRSC2016 and DOTA datasets to demonstrate the effectiveness of the proposed modules and the performance advantages of ReBiDet.