# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 2 Analysis of Image Characteristics  
As depicted in Figure 2, images (a) to (d) illustrate a set of representative complex sea-surface infrared images. The uniformly dark regions enclosed within the red boxes represent the actual targets. In image (a), the background consists of a simple sea surface and the sky. The boundary between the sea surface and the sky is well-defined, and the strong sunlight results in a relatively uniform grayscale distribution across the sea surface, facilitating clear contrast with the targets. Consequently, separating the targets from the background is relatively straightforward, with the primary interference being two low-gray-level regions proximate to the target, exhibiting similar grayscale values. In contrast, the scenes depicted in images (b) to (d) are notably more complex. Due to the influence of sea winds and sunlight, the grayscale distribution of the sea surface becomes highly uneven. Various regions, both bright and dark, of different sizes emerge, and certain dark stripes exhibit grayscale values similar to the target. Moreover, significant areas of islands or landmasses with grayscale values resembling the target are present. In such scenes, conventional threshold segmentation or gradient-based edge detection methods become inadequate for ship target detection. Figure 3 shows the histograms corresponding to the images in Figure 2. It can also be observed from the histograms that the low-gray regions where the targets are located do not exhibit distinct peak points. In other words, there is a gradual grayscale transition between the targets and certain background areas. Simple threshold segmentation can only accomplish the separation of brightness and darkness in the image. Further discrimination and separation are required to detect the targets within the obtained local dark regions that encompass both the targets and the background. Figure 2: Typical complex sea-surface infrared images (**a**–**d**). Similarly, Figure 4 represents the sum of the horizontal row gradients and Figure 5 indicates the sum of the vertical column gradients for each image in Figure 2. Figures 6 and 7 show the sum of the column gradients for each row in the horizontal direction and the sum of the row gradients for each column in the vertical direction, respectively. The calculation of the horizontal and vertical gradients follows Formulas (1) and (2), while Formulas (3) and (4) illustrate the computation of the column gradients for each row in the horizontal direction and the row gradients for each column in the vertical direction, respectively. Formulas (5) and (6) calculate the overall horizontal gradient and vertical gradient, respectively. \[gradx(i,j)=|g(i+1,j)-g(i,j)| \tag{1}\]  
\[grady(i,j)=|g(i,j+1)-g(i,j)| \tag{2}\]  
\[G_{x}(i)=\sum_{j=1}^{N-1}grady(i,j) \tag{3}\]  
\[G_{y}(j)=\sum_{i=1}^{M-1}gradx(i,j) \tag{4}\]  
\[G_{xx}(i)=\sum_{i=1}^{M-1}gradx(i,j) \tag{5}\]  
\[G_{yy}(j)=\sum_{j=1}^{N-1}grady(i,j) \tag{6}\]  
Figure 3: The histograms corresponding to images (**a**–**d**). Figure 2:0 The sum of row gradients corresponding to images (**a**–**d**). Figure 2:1 The sum of column gradients corresponding to images (**a**–**d**). In the provided formulas, \(M\) and \(N\) represent the height and the width of the image, respectively. \((i,j)\) denotes the coordinates of a pixel, \(g(i,j)\) represents the grayscale value of the pixel, _gradx_ and _grady_ are the gradient matrices computed in the horizontal and vertical directions, respectively. \(G_{x}\) represents the sum of vertical direction gradients for each of the three values of the gradient matrices. Figure 2:2 The sum of column gradients for each row in the horizontal direction corresponding to images (**a–d**). Figure 2:3 The sum of row gradients for each column in the vertical direction corresponding to images (**a–d**). each row, and \(G_{y}\) represents the sum of horizontal direction gradients for each column.