# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 3 Experimental Results
### Results and Analysis
#### 3.3.3 Results  
This section primarily presents the performance test results of ReBiDet. We follow the practices commonly employed by researchers in the field of optical remote sensing image object detection. We compare the ReBiDet model with state-of-the-art models reported in recent years on two benchmark datasets, namely HRSC2016 and DOTA, to evaluate and compare its performance. The selected models are from reputable journals and conferences, and we directly quote their performance metrics without modification. The backbone networks used by these models are denoted as R50, R101, R152, H-104, and DLA34, representing ResNet-50, ResNet-101, ResNet-152, 104-layer hourglass network, and 34-layer deep layer aggregation network, respectively. Detailed comparison results for the HRSC2016, DOTA-v1.0, and DOTA-v1.5 datasets are reported in Tables 4-6. On the HRSC2016 dataset, ReBiDet outperforms all existing models, demonstrating outstanding performance compared to the state-of-the-art approaches. In the single-scale detection task on DOTA-v1.0, ReBiDet surpasses all compared methods with a mAP of 76.50%, which is 0.22 higher than the baseline model. In most fine-grained categories, ReBiDet ranks within the top three, except for the ship category, where it achieves the highest AP. In the multi-scale detection task, ReBiDet exhibits various degrees of improvement compared to the single-scale detection results across all categories. ReBiDet also demonstrates improvement in the PL, BD, GTF, SV, SH, TC, ST, SBF, and HA categories compared to the baseline model, with first-place rankings in GTF, TC, ST, SBF, and HA categories. On the DOTA-v1.5 dataset, ReBiDet showcases superior performance compared to the baseline method. This advantage can be attributed to the presence of numerous small objects, approximately 10 pixels or smaller, in DOTA-v1.5, which ReBiDet excels at learning and detecting. Furthermore, even when compared to RTMDet (a research collaboration among Shanghai AI Laboratory, Nanyang Technological University, Tianjin University, Shanghai Jiaotong University, and Northeastern University) [41], ReBiDet exhibits certain advantages in terms of performance and computational parameters. These results indicates the significant capability of ReBiDet in detecting large and elongated objects with ship features. Notably, despite utilizing a 50-layer backbone network, our model outperforms all compared methods with backbone networks of 101 layers or more. Figure 14 presents some results for the DOTA dataset. Figure 13: Comparison of the generalization capability of ReBiDet and other five models trained on the DOTA dataset, using an original image from the FAIR1M [40] dataset. Subfigures (**a–c**) depict the detection results of Rol Transformer [23], R3 Det [39], and S2A-Net [18], respectively, while subfigures (**d–f**) represent the detection results of Oriented R-CNN [20], ReDet [21], and ReBiDet, respectively. With the exception of ReBiDet, all other models were replicated by the authors of the MMRotate framework based on the original papers and the DOTA dataset.