**4. Results**

### *4.1. Qualitative and Quantitative Analyses of Results*

Table 3 shows the comparison of the detection results with the other eight rotated detectors on SRSDD. In Table 3, labels C1–C6 correspond to ore–oil ships, fishing boats, law enforcement ships, dredger ships, bulk cargo ships and container ships. The detection results of the other methods are from Ref. [22]. It can be seen from the results that the performance of our network is better than the eight state-of-the-art methods. In addition, our RBFA-Net achieves the highest mAP with a small model size, which proves the excellent performance of our RBFA-Net. Our RBFA-Net is only half the size of BBAVectors [61]. Moreover, the size of RBFA-Net is smaller than the second-best O-RCNN [62].

For each category, except for fishing boat and dredger, our model obtains optimal or suboptimal results. For fishing boat and dredger, our detection accuracy is also above the average level. For the largest number of categories (bulk cargo) and the lowest number of categories (law enforcement) in SRSDD, our detection accuracy reaches the best, which proves that our network can not only focus on small samples but also ensure the accuracy of large sample targets. It is worth noting that the detection accuracy of our network in law enforcement category is much higher than that of other models. Because law enforcement often appears in a fixed position, it is more sensitive to spatial information. With BAFPN, our network integrates global information, so it is helpful for the detection of such spatial sensitive targets.

**Table 3.** Quantitative evaluation comparison with the eight state-of-the-art detectors. Labels C1–C6 correspond to ore–oil ships, fishing boats, law enforcement ships, dredger ships, bulk cargo ships and container ships.


The best detector is in bold and the second best is underlined.

We also draw the confusion matrix showing the classification results of our network in more detail. The confusion matrix evaluates the classification accuracy of the network. As shown in Figure 10, the abscissa is the prediction category, and the ordinate is the real category. In the confusion matrix, the diagonal is the correct classification probability, and the others are the wrong classification probability. The confusion matrix is composed of ore–oil ships, fishing boats, law enforcement ships, dredger ships, bulk cargo ships, container ships and other class. Among them, the other class includes ships not in the dataset and other sea targets, such as oil platforms.

**Figure 10.** Confusion matrix.

Limited by space, Figure 11 shows the qualitative comparison of second-best method O-RCNN [62] and our RBFA-Net in detail. In order to observe the detection results of densely arranged ships near the shore, we compare the sliced and enlarged SAR images.

From the experimental results, we can draw the following conclusions:


In addition, to better demonstrate the performance of our network, we compare the ground truth, the detection results of the third-best method RoI Transformer (ROI) [31], the detection results of the second-best method O-RCNN [62] and the detection results of RBFA-Net. Figures 12–14 show more detection results. For the ground truth, we show the correct category of each ship. For the test results, the category information and its confidence are displayed in the green label box. The higher the confidence, the greater the possibility that the test result is of this category. In order to display the SAR images more clearly, we increased the brightness of all displayed images.

**Figure 11.** *Cont*. (**a**)

(**c**) 

**Figure 11.** Detailed detection results. (**a**) Ground truth; (**b**) Result of O-RCNN; (**c**) Result of the proposed RBFA-Net.

Figure 12 shows the detection and classification results of the offshore ship. As can be seen from the SAR image, RBFA-Net successfully suppresses the false alarm in the SAR image. ROI and O-RCNN mistakenly identify the interference noise in the SAR image as a bulk cargo target. The detection result shows that RBFA-Net has better scene adaptability. Because our network uses BAFPN, it can fuse and enhance the global information to make RBFA-Net more robust, so as to reduce the impact of noise on the network detection and classification results to a certain extent.

(**c**) (**d**) 

**Figure 12.** Detection results in offshore scenes. (**a**) Ground truth; (**b**) Result of ROI; (**c**) Result of O-RCNN; (**d**) Result of RBFA-Net.

Figure 13 shows the detection and classification results of the inshore ship. It can be seen from the picture that when the complex background occupies most part of the picture, our RBFA-Net successfully detects and classifies the bulk cargo target. O-RCNN and ROI fail to detect the ship targets. This is because our network adopts the alignment module to adjust the sampling position and improve the accuracy of ship detection.

**Figure 13.** Detection results in inshore scenes. (**a**) Ground truth; (**b**) Result of ROI; (**c**) Result of O-RCNN; (**d**) Result of RBFA-Net.

Figure 14 shows the detection and classification results of densely arranged ship scenes. Generally, due to the complex background, false alarm often occurs in the detection results of inshore ships. In addition, the dense arrangemen<sup>t</sup> of inshore ships also brings challenges in detection and classification. From the detection results, we can see that our network not only correctly detects and classifies the densely arranged ships in the nearshore scene but also suppresses the false alarms in inshore scenes. Meanwhile, in the detection results of ROI and O-RCNN, these two networks mistakenly detect the coastal background as bulk cargo targets. This is because we introduce a task decoupling module, which (**a**) (**b**) 

can adjust the feature maps, respectively, for solving the conflict between the regression task and classification task. This adjustment also enhances RBFA-Net's ability to identify background and ship targets.

**Figure 14.** Detection results in densely arranged ship scenes. (**a**) Ground truth; (**b**) Result of ROI; (**c**) Result of O-RCNN; (**d**) Result of RBFA-Net.
