# 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  
Firstly, we conducted ablation experiments on the HRSC2016 dataset to evaluate the aforementioned modules. The average precision (AP) was used as the main evaluation metric, calculated using the 11-interpolated precision method proposed in VOC2007 [35]. AP50 represents the AP value at an IoU threshold of 0.5. As shown in Equation (3),  
\[AP50=\frac{1}{11}\sum_{r\in\{0,0.1,\ldots,1\}}\underset{r\
eq j\
eq r}{max}\,p (\widetilde{r}), \tag{3}\]  
where \(r\) is the recall rate and \(p(\widetilde{r})\) is the precision at recall rate \(\widetilde{r}\). AP75 represents the AP value at an IoU threshold of 0.75. Its calculation formula is the same as that of AP50, except that the IoU threshold is changed to 0.75. AP50 and AP75 are commonly used to evaluate model performance [36]. The mean average precision (mAP) is the average AP calculated at different IoU thresholds ranging from 50% to 95% with a step size of 5%. Its calculation formula is given in Equation (4):  
\[mAP=\frac{1}{\mid T\mid}\sum_{t\in T}AP_{t} \tag{4}\]  
Figure 7: Train loss curve of ReBiDet on DOTA. here, \(T\) represents the set of IoU thresholds, and \(AP_{t}\) is the average precision at IoU threshold \(t\). This calculation method was initially used in the COCO detection challenge (bounding box) and is now commonly used in object detection evaluation [36]. Table 1 presents the performance of each module in ReDet. ReDet represents the experimental results reported by the authors, while ReDet* represents our best performance achieved in the MMRotate framework [34] for comparison. The three added modules showed varying degrees of improvement compared to the baseline. Although AP50 showed a small improvement and even a 0.1% drop after the DPRL module was added, mAP significantly improved. This suggests that the accuracy of the detection results under high IoU thresholds is greatly improved. Our ReBiDet model achieved a 1.27% improvement in mAP compared to the baseline, demonstrating the effectiveness of our proposed method. We further analyzed the detection results of the ReBiDet model on the HRSC2016 dataset and generated a confusion matrix as shown in Figure 8. The test set contained 1228 annotated ground truth boxes. Our model correctly detected 1178 ships, missed 50 ships, and misclassified 106 background images as ships. We were surprised by the misclassification of the background images, but upon visualizing the results, we discovered that incomplete annotations in the HRSC2016 dataset might be the main reason for this. The original author did not fully annotate all objects that should have been labeled as ships in this dataset. Incomplete annotations in the test set can negatively impact model performance during testing. Figure 9 illustrates that the model detected some ships that were not fully labeled during testing. However, due to the incomplete annotation information, the validation program could not accurately determine if these ships were correctly detected, leading to their misclassification as false positives and a decrease in the model's mAP. Considering these findings, we thoroughly examined the HRSC2016 test set and manually re-annotated a portion of the missing targets, adding 80 new target annotations to the existing 1228 annotations. We then re-evaluated the detection performance of ReBiDet on the newly annotated test set. To ensure fairness, all compared models were baseline models from the MMRotate framework, replicated by the authors based on the original papers and the HRSC2016 dataset. Among them, S\({}^{2}\)A-Net-TO [18] was the best model obtained after adjusting the hyperparameters by us. The final experimental results are presented in Table 2. On the new test set, ReBiDet showed slight improvements in mAP, AP50, and AP75. In terms of target detection statistics, at an IoU threshold of 0.5, ReBiDet correctly detected 1256 targets, missed 52 targets, and misdetected 29 targets.