# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 4 Results
### Ship Detection Result
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Learning** & \multicolumn{2}{c}{**Intensity**} & \multicolumn{4}{c}{**Decibel**} & \multicolumn{2}{c}{**Intensity Difference + Texture**} \\ \cline{2-10}
**Rate** & **Recall (\%)** & **Precision (\%)** & **AP (\%)** & **Recall (\%)** & **Precision (\%)** & **AP (\%)** & **Recall (\%)** & **Precision (\%)** & **AP (\%)** \\ \hline \(5\times 10^{-3}\) & 90.58 & 89.14 & 87.06 & 92.53 & 94.06 & 89.89 & 93.51 & 92.90 & 90.47 \\ \(1\times 10^{-3}\) & 91.23 & 93.05 & 87.17 & 94.48 & 94.79 & 92.10 & 94.16 & 95.71 & 91.23 \\ \(5\times 10^{-4}\) & 93.18 & 91.11 & 89.78 & 93.83 & 93.53 & 91.94 & 95.78 & 94.55 & 93.28 \\ \(1\times 10^{-4}\) & 91.56 & 93.38 & 88.27 & 92.86 & 95.33 & 91.25 & 96.75 & 95.51 & 94.86 \\ \(5\times 10^{-5}\) & 92.86 & 90.79 & 89.25 & 94.16 & 94.16 & 92.34 & 97.40 & 94.94 & 95.55 \\ \(1\times 10^{-5}\) & 92.86 & 92.56 & 89.46 & 93.83 & 94.44 & 92.12 & 97.08 & 94.92 & 95.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recall, precision, average precision (AP) at the optimal threshold of each model according to the learning rate. The values in boldface indicate the highest AP index in the corresponding set of images. Figure 12: Test results according to the threshold of the model using intensity difference and texture images. The red graph represents recall, blue graph represents precision, and black graph represents the critical success index. Learning rates were (**a**) 0.005; (**b**) 0.001; (**c**) 0.0005; (**d**) 0.0001; (**e**) 0.00005; (**f**) 0.00001. The dotted line represents the critical value at the maximum critical success index. Figure 14 shows the false ship detections in each detection result. False detection occurred in the ship wakes. This is because the images with the ship wake accounted for only 4% of the total learning images, thereby the learning of the ship wake was not well performed. However, even in such cases, the intensity difference and texture image rarely recognized ship wake as ships. In Figure 14e, it can be observed that false detections occurred in all images. When several ships were very close to each other, it was difficult to distinguish them from the image; therefore, those ships were recognized as a single ship. Figure 15 shows the result of undetected ships in each detection result. The intensity image without preprocessing shows the largest number of undetected ships. The preprocessed image mainly shows false positives in small ships of 15 pixels or less. It can be determined from the detection results that more ships were detected when using the intensity difference and texture images than when using the decibel image. Figure 13: Result of detecting a ship by applying the optimal threshold of each input image in the model with the highest AP index in each training image. Green box: intensity image, blue box: decibel image, and red box: intensity difference and texture image; (**a**,**b**,**d**,**f**–**i**) ships on the sea with sea backscattering effect; (**c**,**e**) ships with strong side-lobe effect. Table 3 shows undetected ships according to size. The size of the ship is taken as the longer side between the width and the length of the ship, and it can be found that a ship with a size of 30-50 pixels accounts for approximately 50% of the total number of ships. The intensity image showed the largest number of undetected cases in most ships regardless of size. Although the number of undetected cases in the decibel image is less than that in the intensity image, undetected cases were found even for large ships, indicating that this image is unstable. In contrast, using the intensity difference and texture images, it can be found that the number of undetected cases is certainly less than with other images. Figure 14: False detection results when the optimal threshold of each input image was applied in the model with the highest AP index in the training image (**a–f**).