# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 4 Results
### Ship Detection Result  
For verifying the effect of the proposed processing method, the model was trained in three ways using (1) SAR intensity images, (2) decibel images, (3) intensity difference, and texture images, respectively. These models were trained using 928 images that consisted of training and validation datasets. To obtain the evaluation results, 232 test images were used. In Figures 10-12, the test results of the trained model using each training image are expressed in terms of the recall, precision, and CSI, according to the threshold. We decided the optimal threshold at the maximum CSI in each test result. As a result, the proposed model has the lowest optimal threshold. It means when training preprocessed images using our proposed method, the deep learning model can differentiate ship objects better. Figure 8: (**a-c**) Normalized intensity image; (**d–f**) sea surface images generated from (**a-c**) in the order of (**b**,**d**,**f**). Figure 9: (**a**) Intensity difference image; (**b**) texture image (**c**) histogram of the intensity difference image; (**d**) histogram of the texture image. The red box in the histogram indicates the distribution band of the ship pixel values. Figure 11: Test results according to the threshold of the model using decibel 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 10: Test results according to the threshold of the model using intensity 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. Table 2 lists the precision, recall, and AP when the optimal threshold value was used for each model. For the model trained using the intensity images, the results were worse than those of the model trained using the preprocessed image in terms of recall, precision, and AP. The precision values of the models trained using the decibel images, intensity difference images, and texture images were similar. However, it can be found that the recall values of the models trained using the intensity difference and texture images were different from the recall value of the model trained using the decibel image by up to 3%. This means that the percentage of undetected ships decreased by 3%. Similarly, when the AP values were compared, it was found that the AP values of the models trained using the intensity difference and texture images were higher by up to 3%. Therefore, when preprocessed images were used in the training process, the ship detection performance was improved. In particular, the preprocessing method proposed in this study was found to be effective in improving the deep training process. Figure 13 shows some of the results when the ships were detected by the learning model having the highest AP in each set of the training images and the optimum threshold value. In Figure 13b, it can be observed that small ships that exist in the sea with strong sea backscatter were detected well. Furthermore, from Figure 13c, it can be observed that ships were distinctly detected despite the presence of a sediment area and a strong side-lobe effect.