# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 3 Experiments
### Experimental Analysis  
In general, we evaluated the performance of object detection methods through multiple evaluation indicators, such as precision, recall, and AP value. Precision, also written as P, indicates how many samples of the predicted result are correct. When P reaches 100%, it means that there is no false detection. Similarly, recall usually remembers R, indicating how many positive samples in the predicted results have been correctly detected. When R reaches 100%, it means that there are no missed targets. To obtain these indicators, the calculation of intersection over union (IoU) is necessary. \[\text{IoU}=A_{o}/A_{u} \tag{21}\]  
where \(A_{o}\) and \(A_{u}\) represent the overlapping and union area of prediction boxes and the ground truth box. Therefore, we need an IoU threshold to determine whether the detection box is correct. Then, true positives (\(TP\)s), false positives (\(FP\)), false negatives (\(FN\)), and true negatives (\(TN\)) in detection results can be found. Consequently, the precision and recall can be calculated; the formula is as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{22}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{23}\]  
Moreover, it is not enough to only evaluate the performance of the model with precision and recall. Because it is possible that when the precision of model A is higher than that of model B, its recall rate is lower than that of model B. The solution to this problem is to combine precision and recall to calculate another indicator, mean average precision (AP) score--where m means the average of multiple categories. Quantitatively, AP means the average value of precision for each object category when recall varies from 0 to 1. It can characterize the area under the precision-recall curve. Compared to F1, AP can more accurately and intuitively reflect the performance of the detection model. For the ship detection task discussed in this paper--because only a ship needs to be detected--mAP is equal to AP. **Analysis of different schemes. ** This section focus on verifying the superiority of the proposed schemes, including scene classification, saliency detection, and disentangled non-local modules. As presented in Table 2, methods 1-5 are constructed models with different schemes. The first row is the baseline method Yolov4, and the second method is the baseline algorithm with saliency detection module. Other methods add different modules in turn to verify the validity of each scheme. It can be seen that each method performs better than the previous one. When all three schemes are included in a model, the AP can be improved to 87.05%. Consequently, the proposed schemes in this paper are all effective. Figure 6: Schematic diagram of simulation sample production with randomly added different types of ships in the background in order to obtain augment samples. **Number of DNL analysis. ** The DNL module can extract more salient features, but it also means that the network is prone to overfitting when a large number of modules are added. Thus, in order to obtain a better detection result, the number of DNL added into the YOLO network needs to be adjusted. Figure 7 displays the relationship between the number of DNL in the network and the average precision. We can see that the average precision is highest when the number of DNL modules is 12. After that, as the number of DNL increases, average precision will decrease instead. Simultaneously, we have observed experimentally that as DNL increases, the loss of verification dataset will also increase. It indicates that the network learning has overfitted for this training dataset. **Confidence score analysis. ** As a key parameter, the confidence score has a great influence on the final result. A higher confidence score usually means less FPs; it may increase precision, but also decrease the recall. Thus, to obtain a better trade-off between detection and location accuracies, the confidence score threshold in this paper is set 0.5. The IoU threshold is also set 0.5, according to experiments.