## 4 Experimental Results
### Results of TIR Ship Detection
#### 4.2.2 Visual Comparison to TIR Ship Target Detection Baseline Methods  
In this part, four classical ship target detection methods and four state-of-the-art ship target detection methods are introduced in the comparison experiments to evaluate the performance of the proposed TIR ship target detection method. The top-hat/bot-hat filters (THF/BHF) [6,35], 2-D maximum entropy (2 DME) [17], 2-D Otsu (2 DO) [15] and mean shift segmentation (MSS) [19] are chosen as representative classical ship target detection methods. The iterative multi-feature segmentation (IMFS) [23], improved fuzzy C-means clustering (IFCM) [3], Chan-Vese model (CVM) [21] and histogram cyclic shift transformation (HCST) [13] are selected as representative state-of-the-art methods. Because those methods have been well studied, they can be used for assessing the performance of the new ship target detection method. Figure 13: The results of the average eigenvalue measure of structure tensor (STAEM) values of the blocks and the experimentally selected distinctive threshold. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequences** & **Seq1** & **Seq2** & **Seq3** & **Seq4** & **Seq5** & **Seq6** & **Seq7** & **Seq8** & **Seq9** \\ \hline Image size & 640 x 480 & 640 x 480 & 640 x 480 & 320 x 240 & 640 x 480 & 640 x 480 & 640 x 480 & 640 x 480 \\ Sea clutter complexity & Medium & Medium & High & Medium & High & High & Low & High \\ Target area & 112-308 & 355-513 & 323-487 & 781-1557 & 95-591 & 246-767 & 195-304 & 279-1034 & 6–50 \\ Target brightness & Bright & Bright & Bright & Dark & Dark & Dark & Bright & Bright/dark & Bright \\ Target number & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 5 \\ Total images & 500 & 500 & 500 & 500 & 500 & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The detail information of the test image sequences. Figure 14 shows the ship target detection results of different methods for Figure 12. In the detection result images of Figure 12i, the point ship targets are enlarged for better observation and marked by red boxes. The classical THF/BHF is a commonly used method for background suppression and target enhancement for its simple computation and easy implementation. However, it is vulnerable to heavy clutter and may not perform well for ship targets submerged in heavy sea clutter. It can be seen from Figure 14b1-b9, the detection results of classical THF/BHF can perceive the ship targets but suffer from extremely serious residual clutter. The 2 DME and 2 DO both are threshold processing methods based on 2-D histogram analysis of the image. When the contrast between the ship target and the background is high, the two methods can be directly used to segment the ship targets. However, these methods are sensitive to the contrast and pixel percentage of ship target and background. Therefore, it can be seen from Figure 14c1-c9,d1-d9 that those two methods obtain the worst detection results in all nine scene images. The MSS is a feature-space analysis algorithm based on graph region merging, so it can efficiently extract the regions of ship targets against a homogenous background. Nevertheless, the MSS does not work well for ship target detection in infrared images with heterogeneous background, as shown in Figure 14e1-e8. More importantly, the MSS method cannot detect point-size ship targets because the point ship targets will be eliminated when regions are merging, as Figure 14e9 illustrates. The IMFS method reasonably uses two approaches including iterative global thresholding and ship target shape constraint to segment ship targets, hence the IMFS presents better performance than the above four classical methods, as Figure 14f1-f3,f7-f9 shows. The IMFS is based on the assumption that the ship target region is much brighter than the background, so it completely discards the true dark ship targets buried in a brighter sea background, as shown in Figure 14f4-f6.