# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 3 Experimental Results
### Results and Analysis
#### 3.3.1 Analysis Based on HRSC2016 Dataset
Its precision, recall, and accuracy were significantly better than those of ReDet and other detection models. \begin{table} \begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**ReBiFPN**} & **DPRL** & **Anchor Im-** & **mAP** & **AP50** & **AP75** \\  & & **Sampler** & **provement** & **mAP** & **AP50** & **AP75** \\ \hline ReDet & - & - & - & 70.41  & 90.46  & 89.46  \\ \hline  & - & - & - & 72.34  & 90.30  & 88.50  \\ \cline{2-6} ReDet * & ✓ & - & - & 72.80 & 90.40 & 89.20 \\  & - & ✓ & - & 72.86 & 90.20 & 89.10 \\  & - & - & ✓ & 72.41 & 90.40 & 89.10 \\ \hline ReBiDet (ours) & ✓ & ✓ & ✓ & 73.61 & 90.50 & 89.80 \\ \hline \hline \end{tabular}   
*In terms of Precision, ReBiDet outperformed \(\mathbb{S}^{2}\)A-Net by 6.8% at an IoU threshold of 0.5, but the difference in AP50 was only 1%. This can be attributed to the lower precision of ReBiDet at certain recall rates, affecting its overall AP50. Compared to RetinaNet + KLD, ReBiDet had only a 1.04% higher precision, but its precision at certain recall rates was much higher, resulting in a 2.1% higher AP50 than RetinaNet+KLD. This demonstrates that AP \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Method** & **mAP** & **AP50** & **AP75** & **TP** & **FN** & **FP** & **TN** & **Precision** & **Recall** & **Accuracy** \\ \hline RetinaNet-O [37] & 52.51 & 86.80 & 59.20 & 997 & 311 & 67 & 0 & 93.70 & 76.22 & 72.51 \\ RetinaNet-O [37] + KLD [38] & 55.43 & 88.70 & 62.00 & 1054 & 254 & 36 & 0 & 96.70 & 80.58 & 78.42 \\ \(\mathbb{S}^{2}\)A-Net [18] & 55.31 & 89.80 & 58.90 & 1185 & 123 & 118 & 0 & 90.94 & 90.60 & 83.10 \\ \(\mathbb{S}^{2}\)A-Net-TO [18] & 61.00 & 90.20 & 71.10 & 1185 & 123 & 63 & 0 & 94.95 & 90.60 & 86.43 \\ ReDet [21] & 72.46 & 90.70 & 89.70 & 1243 & 65 & 44 & 0 & 96.58 & 95.03 & 91.94 \\ ReBiDet (ours) & 73.66 & 90.80 & 90.00 & 1256 & 52 & 29 & 0 & 97.74 & 96.02 & 93.94 \\ \hline \hline \end{tabular} Note. The model configurations and parameters of RetinaNet OBB (RetinaNet-O) [37], RetinaNet-O [37]+ KLD [38], \(\mathbb{S}^{2}\)A-Net [18], and ReDet [21] were all trained by the MMRotate team based on HRSC2016. We did not make any modifications to them. \(\mathbb{S}^{2}\)A-Net-TO is a model that we optimized based on the MMRotate framework.  
\end{table} 
Table 2: Comparison of detection results on the re-annotated HRSC2016 test set. All models compared are baseline models from the MMRotate framework. Figure 8: Confusion matrix of the detection results of the ReBiDet model on the HRSC2016 dataset. The numbers in the figure refer to the number of targets. Figure 9: Comparison between the ground truth label and detection result of an image in the test set of HRSC2016. (**a**) Image annotated with the ground truth label. (**b**) Our detection result on the same image. and precision are not directly related [36]. AP is a more comprehensive indicator, as it considers multiple factors and evaluates the model's performance under different conditions [36]. Compared to the baseline model ReBDet, ReBiDet consistently outperformed in AP50, AP75, and mAP, indicating its superior detection accuracy under high IoU threshold conditions. This further validates the superiority of our proposed ReBiDet model.