# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 3 Detection Algorithm
### Local Low Gray Region Extraction and Segmentation
#### 3.2.2 Dual-Channel Segmentation and Multi-Feature Discrimination
Finally, (d) displays the outcome of assessing the area ratio based on (c). Firstly, regarding the adaptive segmentation of (d) in Figure 9, there are small patches of interference on the sea surface that have grayscale values similar to the target. Consequently, after segmentation, fragmented connected regions are generated. Since our focus is on patches of a certain size as targets, regions with fewer than 81 pixels or more than 1500 pixels are filtered out, resulting in the outcome shown in (b) of Figure 10. Secondly, due to the irregular nature of the sea-land boundary, the segmentation process produces multiple interfering regions. From (b), it is evident that some interference regions differ in length or width compared to the target, leading to dissimilar aspect ratios. By utilizing the aspect ratio, we can effectively filter out long coastlines or thin dark bands. The remaining interference regions in this case exhibit a rectangular shape and are relatively compact, so measures such as Rectangularity and Compactness do not significantly contribute to their filtering. However, the interference clutter on the sea surface displays a higher degree of randomness in terms of shape, making the application of these criteria useful for filtering such clutter. Lastly, typical ship targets often exhibit a distinctive \"smaller on top and larger on the bottom\" shape characteristic. Exploiting this characteristic, the candidate target region is divided vertically into two parts. If the area of the bottom part is greater than that of the top part, it is considered a candidate target; otherwise, it is classified as an interference region and subsequently filtered out. From (d), it can be observed that the \(Area_{ratio}\) criterion successfully removes the interference region on the far left of (c). However, interference from the coastline region remains, necessitating the fusion and filtering of results from another channel. In the other channel, to reduce computational complexity and consider that the segmentation performed on the preprocessed image is a coarse segmentation aimed at obtaining approximately grayscale regions, it is sufficient to filter out the segmented regions based on their area. The dual channel segmentation process is depicted in Figure 11.  
Figure 10: Schematic diagram of the multi-feature discrimination process. (**a–d**) corresponds to the result of different processing stages. The dual-channel threshold segmentation technique is primarily employed to address the presence of small islands and reefs with a similar grayscale, shape, or irregular partial boundary areas between the sea and land, commonly encountered in complex sea surface backgrounds. Upon examining the dual-channel segmentation process depicted in Figure 9 of the paper, it is evident that even after segmenting and applying multi-feature discrimination to the low gray area detection results, there may still be interference from islands and reefs resembling the target. This issue arises because the convolutional template used for extracting the low gray area filters out regions with narrow shapes and small areas, thereby preventing the elimination of isolated island and reef interferences through multi-feature judgment alone. In the second channel, the inverted preprocessed image adopts a coarse segmentation strategy, causing the reefs to connect with the surrounding low-gray area, forming a larger area that can be eliminated by applying an area threshold after threshold segmentation. By subsequently multiplying and fusing the results from the two channels, accurate ship target detection results are obtained. This approach effectively addresses the challenge of interference from islands and reefs, leading to improved accuracy in ship target detection within complex sea surface backgrounds.