# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 3 Experiment and Comparison
### Analysis of Experimental Results  
During the experiment, the free satellite remote sensing data disclosed by Google Earth was selected. When downloading the data, the options of level 13 (in-space resolution: 38.22 m), Level 14 (resolution: 19.11 m), and Level 15 (resolution: 9.55 m) were selected. Download and make data sets for offshore and ocean-going areas in Asia. According to low-resolution (30 m), offshore, cloud-through and normal targets, the data were divided into four types for separate testing. The experiment tested 20 groups (denoted as \(G\)), a total of 200 images (SNR \(\geq\) 3), and there was a total of 2670 targets. A total of 80% of the data was used for training and 20% for testing. Among them, groups 1 to 5 were small ship targets of about 30 m, which were denoted as class \(G_{1}\); groups 6-10 were the target of sailing ashore, which were denoted as class \(G_{2}\); groups 11-15 were the ship targets passing through the cloud layer, which were denoted as class \(G_{3}\); groups 16 to 20 were conventional sea surface ship target scenes, denoted as class \(G_{4}\). Table 2 and Figure 6 show the performance comparison between Yolo Tiny V2/V3 in references [7; 8] and the results obtained by the proposed algorithm. Yolo Tiny V2/V3 algorithm only depends on the image feature information of the ship target itself, so the detection effect of the docking target (class \(G_{2}\)) and small target (class \(G_{1}\)) was poor. The docking target was caused by the interference caused by artificial construction, buoys, artificial islands, and berthing ships near the port. Figure 7 shows the detection results of remote-sensing ship targets in different scenarios. The proposed algorithm outperformed the traditional lightweight Yolo algorithm in different scenarios. The main reason was the four-level cascaded authentication architecture. In the first stage, false alarms with the large area were eliminated through front and rear scene segmentation; in the second stage, false alarms with significant differences between training targets and scene features were destroyed; in the third stage, non-moving wrong alarm targets were eliminated through multi-frame cascade search; in the fourth stage, false alarms were further eliminated through fine feature discrimination among the remaining suspected targets. Table 3 and Figure 8 compare the recall ratio, recall ratio, and F1 number of different discrimination steps of the design method in this paper. It can be seen that simple preprocessing and S-HOG feature discrimination cannot eliminate false alarms well. On the other hand, association effectively improved detection accuracy by 15%, and S-YOLO discrimination improved each index by 5%. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**No.** & **Model** & \multicolumn{2}{c}{\(\mathbf{G_{1}}\)} & \multicolumn{2}{c}{\(\mathbf{G_{2}}\)} & \multicolumn{2}{c}{\(\mathbf{G_{3}}\)} & \multicolumn{2}{c}{\(\mathbf{G_{4}}\)} & \multicolumn{2}{c}{\(\mathbf{G}\)} \\ \hline \multirow{2}{*}{1} & Yolo Tiny V2 [7]} & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ \\ \cline{2-13}  & 0.87 & 0.93 & 0.90 & 0.92 & 0.81 & 0.86 & 0.95 & 0.91 & 0.93 & 0.97 & 0.97 & 0.93 & 0.91 & 0.92 \\ \hline \multirow{2}{*}{2} & Yolo Tiny V3 [8] & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ \\ \cline{2-13}  & 0.87 & 0.97 & 0.93 & 0.88 & 0.88 & 0.95 & 0.88 & 0.91 & 0.97 & 0.90 & 0.93 & 0.92 & 0.91 & 0.92 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Proposed} & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ & _Pt_ & _Rc_ & _F1_ \\ \cline{2-13}  & & 0.91 & 0.97 & 0.94 & 0.98 & 0.93 & 0.95 & 0.89 & 0.96 & 0.93 & 0.97 & 0.99 & 0.98 & 0.93 & 0.97 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of 20 groups of remote sensing data test results [_Pt_, _Rc_, _F1]. Multi-frame association reduced false alarm interference by predicting the trajectory of at least three frames and the nearest target judgment.