# 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  
Earlier, we noted that the authors of DOTA-v1.0 did not provide ground truth annotation files for the test set. Instead, participants were required to submit their test results to a designated server, receiving mAP and AP values for each class as feedback. However, these metrics were insufficient for a comprehensive performance analysis. In our experiments, we trained ReBiDet and ReDet models on the training set and evaluated them on the validation set, which was not used for training. The MMRate framework was employed for training and evaluation. The ReDet model's learning rate was set to 0.01, consistent with ReBiDet. Other settings for ReDet remained at their default values. To facilitate a more intuitive comparison of detection performance, we generated confusion matrices for both models. Due to the large number of objects per class in the test set, we normalized the confusion matrix rows to present the correct and false recognition rates as percentages. The diagonal line in the matrix represents the proportion of true positive (TP) detections, while the rightmost column represents the number of false negative (FN) detections. Other areas denote false positive (FP) detections. Figure 10 illustrates that ReBiDet achieved a 92% TP rate for the ship class, surpassing ReDet by 1%, while also exhibiting a 7% FN rate, 1% lower than ReDet. For other classes, ReBiDet demonstrated either higher or equivalent TP numbers compared to ReDet, along with generally significantly lower FN numbers. This further confirms the effectiveness of our proposed modules. Figure 11 compares the ground truth labels of some validation set images with the detection results of the ReBiDet model trained solely on the training set. The images demonstrate the superior ability of ReBiDet to detect objects not annotated in the original dataset. Figure 11,b showcase the strong performance of ReBiDet in detecting densely packed vessels, while Figure 11,d exemplify its recognition capability for vessels in motion. Figure 11,f,k,l demonstrate the effectiveness of ReBiDet in detecting small objects with in  
Figure 10: Normalized confusion matrix on the DOTA dataset. (**a**) The detection results of ReDet reproduced by us. (**b**) The detection results of ReBiDet. The unit of the numbers in the figure is percentage (%), representing the percentage of ground truth label targets. distinct features. Furthermore, Figure 11g-j demonstrate the robust detection performance of ReBiDet, even in complex scenes with cloud and mist interference. Additionally, when comparing ReBiDet with other models on a single RTX4090 computing platform, ReBiDet exhibited a slightly higher computational parameter count while achieving superior performance. Table 3 presents the comparison of ReBiDet with other models in terms of computational parameters, inference speed, and other dimensions. Within the MMRate framework, ReBiDet had 32.56 million computational parameters, merely 0.92 M more than the 31.64 M of ReDet. This less than 1 M increase resulted in an additional 2.5 ms per image during model inference, corresponding to a decrease of 1.4 img/s in FPS. Thus, ReBiDet incurred a small computational cost, while significantly improving detection accuracy. The table reveals that computational parameters and inference speed are not completely correlated, with models having similar parameters but different inference speeds, and vice versa. Specifically, although ReBiDet had a relatively small parameter count, its inference speed was not fast enough. This may be attributed to the backbone network ReResNet50 and ReBiFPN, which are constructed based on E2 CNN. These networks utilize rotation-equivariant convolutional layers, involving additional operations, such as rotation, interpolation, and grouping, which can reduce the calculation speed.