# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 2 Analysis of Image Characteristics
\(G_{xx}\) represents the sum of horizontal gradients for each row, and \(G_{yy}\) represents the sum of vertical gradients for each column. From Figure 4, it can be seen that in horizontally uniform regions with consistent grayscale values, the sum of row gradients is relatively small, such as the areas representing the sea-sky interface and horizontally distributed dark bands. However, the overall distribution of row gradient sums is not uniform. Except for the noticeable transition region in Figure 4d, the remaining images do not exhibit significant variations, making it difficult to segment the images based on row gradient sums. In the vertical direction, the grayscale distribution of the images is even more uneven. Whether we examine the column gradient sums in Figure 5 or the sum of row gradients in the vertical direction in Figure 6, the gradient sums are generally large and exhibit a chaotic distribution, lacking separability. Figure 6 reveals that there are rows with the maximum sum of vertical gradients in certain images, allowing for the detection of the sea-sky interface or the approximate row position of the targets. However, for the sea surface portions, the sum of the vertical gradients for each row is generally large, making it insufficient to differentiate between the targets and the noise caused by waves. Most image saliency analyses start from the image's grayscale and gradients. But based on the analysis above, saliency maps calculated using grayscale or gradients will inevitably contain much interference. In fact, when there are regions in the image that are brighter or darker than the target, the computed saliency map may not include the target. However, from the examples of typical infrared images of the sea surface, we can observe that within a certain imaging distance, the grayscale distribution of the target area is relatively uniform and mostly approximates a rectangular shape. Although there are some regions of interference with an even grayscale, the interference areas are irregular and constantly changing in size and shape due to the continuous fluctuation of the waves. Based on the analysis above, considering the low grayscale characteristics of the targets, this paper proposes a method based on detecting local low grayscale regions. Specifically, a convolutional kernel with a negative value at its center is used to traverse the preprocessed image, aiming to identify regions with low gray levels. Subsequently, adaptive threshold segmentation is applied to these identified regions, leading to refined segmentation outcomes by incorporating shape, area, and other features. Furthermore, given the substantial grayscale difference typically observed between the sky background or land terrain background and the boundary of the sea surface, the proposed convolutional template may yield relatively high grayscale values in this region, introducing interference with the targets. Conversely, the grayscale values across the entire mountain or sky background region are similar. By performing adaptive segmentation on the preprocessed image, a connected region representing the mountain or sky background, which is significantly larger than the target, can be obtained. Therefore, by considering the area feature, the mountain or sky background can be eliminated to obtain a preliminary segmentation result. Consequently, by combining the refined segmentation result with the preliminary segmentation result, accurate detection outcomes can be achieved.