# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 3 Experimental Results
### Results and Analysis
#### 3.3.2 Analysis Based on DOTA-v1.0
Additionally, unlike traditional ResNet employing PyTorch's built-in convolutional layers, ReResNet50 and ReBiFPN require calling the _e2 cnn_ library for rotation-equivariant network calculations, potentially affecting the computational efficiency. Figure 11: Comparison between the ground truth label and detection result of images in the validation set of DOTA-v1.0. (**a,c,e,g,i,k**) are image annotated with the ground truth label. (**b,d,f,h,j,l**) are our detection result on the same image. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **Backbone** & **SH** & **mAP** & **Params** & **FPS** & **TPI (ms)** \\ \hline \hline \multicolumn{1}{l}{Rolololololololumn{1}{l}{Transformer * [23]} & ResNet-101 & 83.59 1 & 69.56 1 & 74.12 & 30.00 & 33.30 \\ \multicolumn{1}{l}{R3 Det [39]} & ResNet-152 & 78.21 1 & 73.74 1 & 76.54 & 20.40 & 49.10 \\ \multicolumn{1}{l}{S2_A_-Net [18]} & ResNet-50 & 87.25 1 & 74.12 1 & 87.25 & 32.10 & 31.20 \\ \multicolumn{1}{l}{Oriented R-CNN [20]} & ResNet-50 & 88.20 1 & 75.87 1 & 41.14 & 39.30 & 25.40 \\ \multicolumn{1}{l}{Oriented R-CNN [20]} & ResNet-101 & 87.52 1 & 76.28 1 & 60.13 & 32.00 & 31.30 \\ \multicolumn{1}{l}{ReDet [21]} & ReResNet-50 & 88.04 1 & 76.25 1 & 31.64 & 24.70 & 40.50 \\ \multicolumn{1}{l}{ReBiDet} & ReResNet-50 & 88.41 & 76.50 & 32.56 & 23.30 & 43.00 \\ \hline \hline \end{tabular}  
* indicates multi-scale training and testing. 1 The detection results published by the original paper authors. Note. SH refers to the AP of the ship category. Params refers to the computational parameter quantity of the model, in million units. FPS refers to the number of images detected per second by the model during inference. TPI is an abbreviation for times per image.  
\end{table} 
Table 3: Comparison of ReBiDet with other models on computational parameters and inference speed. It is important to note that ReBiDet also exhibits excellent generalization performance. Figure 12 presents the detection results when applying the ReBiDet model trained on the DOTA dataset to image from the HRSC2016 dataset. The ReBiDet trained on the DOTA dataset outperformed the one trained on HRSC2016, showing improved alignment between the labeled detection boxes and the actual targets. ReBiDet detected more ships, including three additional small-sized vessels. This improvement can be attributed to the HRSC2016 training dataset's incomplete annotation, which hinders model training completeness and leads to subpar detection performance. Additionally, the ReBiDet model underwent specific optimizations for ship detection tasks, particularly enhancing feature extraction capabilities through the ReBiFPN module. These improvements contribute to better generalization performance following thorough training. To further assess the models' generalization capability, we selected an image from the FAIR1M [40] dataset. To ensure fairness, all compared models were baseline models from the MMRotate framework, reconstructed by the authors based on the original papers and the DOTA dataset. Figure 13 reveals that only ReBiDet and RoI Transformer [23] detected the small vessel on the left side of the image. However, RoI Transformer mistakenly identified three land objects as ships. Although R\({}^{3}\)Det [39] detected more ships, most of the annotated detection boxes had confidence scores below 0.5. \(\lx@math@degree{S}^{2}A\)-Net [18] exhibited a similar situation, with confidence scores around 0.5 for the detected large ships, indicating uncertainty in the results and potentially affecting the AP metric. Oriented R-CNN [20] falsely detected a land object, and ReDet [21] failed to detect an adequate number of ships. For this particular image, ReBiDet demonstrated superior generalization capability. Figure 12: Illustration of the generalization ability of the ReBiDet model trained on the DOTA dataset, using an original image from the HRSC2016 test set. Subfigure (**a**) displays the detection results obtained by the ReBiDet model trained on the HRSC2016 dataset.