# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 3 Experiments
### Comparisons on Detection Task with Other Methods  
To illustrate the performance of the proposed saliency-tuned YOLONet ship detection method, we conducted comparative experiments and compared them with some other state-of-the-art object detection approaches. These included two one-stage methods, YOLOv4 citeyolov4 and SSD [33], and two-stage methods, Faster-RCNN [28] and SPPNet [26]. Because the variation of the object size is large and some ships are small, the IoU and confidence threshold were set to 0.2 and 0.5, respectively, to obtain better results. We trained the data samples over 100 epochs, and all other models were tuned to optimal parameters in order to achieve the best results.  
**Precision-recall curve analysis.** The precision-recall curves and AP scores of different methods on the Nan-Hai dataset are shown in Figure 9 and Table 3, respectively. The recall threshold is set from 0 to 1. In addition, to ensure the fairness of the experiment, we try our best to ensure that all models are trained optimally. Table 3 shows that the detection precision rate and recall rate of the proposed method is higher than the compared methods. For the comprehensive index AP, the evaluation index demonstrates that it can reach 87.05%. This AP is significantly higher than that of the other detectors investigated. Moreover, the red line in Figure 9 can more intuitively show that our method has a larger area under the line. This can be attributed to the fact that the method proposed in this paper indeed have better detection performance.  
Figure 8: Ship detection precision (%) versus confidence score for different detection methods. (**a**) YOLOv4, (**b**) Faster RCNN, and (**c**) our method.  
**Comprehensive analysis.** We tested the model with images of size 2048 \(\times\) 2048. Large-format images can better reflect the performance of network detection in various sea scenes. At the same time, in order to verify the generalization ability of the detection method, simulation samples of different types and sizes are added in some images. The comparative experimental results of the different methods are illustrated in Figure 10. Among them, the green box represents the ship targets in the original images, and the red box represents the detection results. It can seen from this figure that all methods can obtain good detection results for cloudless scenes. However, when the scene is more complex, such as those containing cloud interference and occlusion, SPPNet has a higher false alarm rate. This is partly because the network does not extract enough features, which leads to false detections. However, compared to the other two methods, YOLONet and Faster RCNN, it can detect relatively more ship targets. For our method, although it adds some false alarms, more accurate detection results can also be obtained. Over all, the network model proposed in this paper has a better detection performance.  
Figure 9: Ship detection precision (%) versus recall (%) for different detection methods. The larger the area under the curve, the better the model performance.