# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 1 Introduction
### Related Works  
After years of research, several mature methods have been developed for ship detection. Early studies predominantly focused on ship detection methods based on artificial feature modeling. However, since 2012, deep learning-based detection methods have emerged as a transformative force in the field of ship detection. Recent years have witnessed the proposal of numerous methods for ship detection in remote sensing images. Leveraging the powerful feature expression capabilities of convolutional neural networks (CNNs), these deep learning-based object detection methods can extract higher-level semantic features of ships, delivering significantly improved performance in complex remote sensing scenarios compared to traditional approaches. Nevertheless, most of these methods originated from object detection models designed for natural images captured from horizontal viewpoints and are limited in their ability to detect objects with arbitrary orientations. Given that ships possess relatively large aspect ratios and can exhibit random orientations in images, employing horizontal detection boxes would result in significant background information being included in the detection boxes. This issue becomes particularly pronounced when detecting densely distributed ships in a harbor, where interference from neighboring objects leads to poor detection performance. To address these challenges, researchers have proposed models suitable for detecting objects with arbitrary orientations. For instance, the rotational R-CNN (R2 CNN) model [14], initially developed for scene text detection, has shown excellent performance in detecting slanted text and has been adapted for ship detection. [PERSON] et al. [15] enhanced this approach by introducing a rotation region of interest (RoI) pooling layer and a rotation bounding box regression module based on R2 CNN. This improvement enables accurate feature extraction and localization, resulting in highly efficient and accurate ship detection. The rotated region proposal network (RRPN) [16] was designed as an enhancement of R2 CNN to improve the quality of candidate regions generated by the region proposal network (RPN). RRPN generates 54 rotating anchor boxes with 3 scales, 3 aspect ratios, and 6 directions at each point in the feature map. [PERSON] et al. [17] incorporated the dense feature pyramid network (FPN) based on RRPN to integrate low-level positional information and high-level semantic information, effectively detecting densely distributed ships using prow direction prediction. They also reduced redundant detection areas using rotational non-maximum suppression (NMS) to obtain more accurate object positions. [PERSON] et al. [16] proposed the one-stage model CHPDet, which utilizes an orientation-invariant model to extract features. It selects the peak value of the feature map as the center point and then regresses the offset, size, and head point on the corresponding feature map of each center point, achieving impressive results in terms of speed and accuracy. [PERSON] et al. [18] introduced a module called RoI Transformer based on the Faster R-CNN [13]. This module predicts a rough rotating RoI in the first stage based on the horizontal RoI generated by RPN. In the second stage, RoI Align [19] extracts the feature map of the RoI and refines the prediction result of the first stage to obtain a more accurate rotating RoI. Compared to RRPN, RoI Transformer significantly reduces the number of rotating anchors, leading to improvements in computational efficiency and detection accuracy. Oriented R-CNN [20], building upon the Faster R-CNN, introduced the midpoint offset representation and expanded the output parameters of the RPN regression branch from four to six to achieve skewed candidate boxes, resulting in substantial improvements in detection accuracy and computational efficiency. Most object detection models are based on traditional CNNs, which possess translational equivariance but lack rotational equivariance.