# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 4 Experimental Results and Analysis
### Qualitative Contrast Analysis  
In Figure 13, numbers (1)-(6) are the serial numbers for different scenes, (a), (b), (c) and (d) represent the detection results of the ISSD algorithm, ITDBE algorithm, SRISTAS algorithm, and the algorithm proposed in this paper for the leftmost column image, respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence Number** & **Frame Number** & **Image Size** & **Numbers of Targets per Frame** \\ \hline
1 & 650 & 640 \(\times\) 512 & 2 \\
2 & 300 & 640 \(\times\) 512 & 1 \\
3 & 400 & 640 \(\times\) 512 & 1 \\
4 & 400 & 640 \(\times\) 512 & 1 \\
5 & 500 & 640 \(\times\) 512 & 1 \\
6 & 320 & 640 \(\times\) 512 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information about each test sequence. Figure 12: (a–f) corresponds to sequence 1–6. The ISSD algorithm essentially highlights the target by taking the difference between the image and the background, which means that it emphasizes areas where the target has significant contrast with the surrounding region. Consequently, when the grayscale values of the target and the neighboring background are relatively consistent but different, the extracted target features become more salient, making them easier to segment. This characteristic can be discovered in the segmentation results of the ISSD algorithm for the selected six sequences' typical images. However, in the image of sequence 6, there is a similar interfering region on the left side of the target area. Despite considering multiple geometric feature measurements, the ISSD algorithm fails to eliminate this interference. Figure 13: (**a–d**) represent the detection results of each algorithm for typical images in each sequence. The ITDBE algorithm uses Gaussian difference preprocessing to the image to highlight the target, so it performs better in regions with high contrast. This is demonstrated by the detection results of typical images in sequence 1, sequence 2, and sequence 3. However, this method selects only one saliency map that best highlights the target for segmentation. In some scenarios, the most salient map may not be fixed, making it challenging to accurately segment the target, as shown by the segmentation results of typical images in sequences 4 and 6. Furthermore, the ITDBE algorithm does not incorporate geometric feature quantities to discriminate the segmentation results. As a result, it tends to produce more false alarms in complex scenes. The detection performance of the SRISTAS algorithm depends on the position of the located rows. If there are no suitable gradient values in the target region, it may result in missed detection. On the other hand, if multiple regions with significant gradient changes are present, it may lead to several regions for segmentation. Additionally, this method implements segmentation based on the mean grayscale values of rows and columns. Therefore, when there are regions of interest with uniform grayscale values but significant differences from other regions, such as cloud clusters, island backgrounds, or wave interferences with similar grayscale values to the target, the detection results may contain many false alarms. We can see it from the detection results of sequences 1, 2, 4, 5, and 6 easily. Furthermore, the SRISTAS algorithm only uses aspect ratio and target area to eliminate false alarms, which may not be effective in all cases. It is evident that in complex backgrounds, there are often regions that resemble the target, and existing algorithms may struggle to remove these interferences perfectly. Therefore, it is crucial to combine multiple geometric feature measurements of the target to discriminate and improve the detection accuracy. The algorithm proposed in this paper achieves target detection by combining the coarse segmentation of the image and the fine segmentation of low-intensity regions detected and utilizing multiple geometric feature measurements of the target. In the provided test sequences, the proposed algorithm demonstrates accurate detection results.