# Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
## 4 Experimental Results and Analysis
### Quantitative Contrast Analysis  
To objectively evaluate the performance of the proposed algorithm, this paper adopts widely used metrics, including true positive rate (TPR), false alarm rate (FAR), misclassification error (ME), and relative area error (RAE), as evaluation criteria. The calculation methods for these four metrics are as follows:  
\[ME=1-\frac{|B_{o}\cap B_{T}|+|F_{o}\cap F_{T}|}{|B_{o}|+|F_{o}|} \tag{17}\]  
\[RAE=\left\{ \begin{array}{ll}\frac{A_{o}-A_{T}}{A_{o}},&A_{o}>A_{T}\\ \frac{A_{T}-A_{o}}{A_{T}},&\text{others}\end{array} \right. \tag{18}\]  
\[TPR=\frac{TP}{GT} \tag{19}\]  
\[FAR=\frac{FP}{FP+TP} \tag{20}\]  
where \(B_{o}\) and \(F_{o}\) represent the number of background and target pixels in the ground truth image, respectively. \(B_{T}\) and \(F_{T}\) represent the number of background and target pixels detected by the algorithm, respectively. \(A_{o}\) denotes the size of the ground truth target region and \(A_{T}\) represents the size of the detected target region. \(TP\) represents the number of correctly detected true targets and \(GT\) represents the number of actual targets in the reference image. \(FP\) represents the number of background pixels falsely detected as targets and \(TP\) represents the number of true targets correctly detected as targets. The misclassification error (ME) reflects the ratio of background pixels misclassified as foreground and foreground pixels misclassified as background. The relative area error (RAE) indicates the degree of closeness between the size of the detected target and the actual target. The true positive rate (TPR) reflects the ability of an algorithm to detect targets from the given image, while the false alarm rate (FAR) indicates the proportion of non-true targets among the detected targets. Analysis of the above four formulas shows that the lower the ME, FAR, and RAE values, along with a higher TPR value, the better the detection and segmentation performance of the algorithm. Tables 2 and 3 present the ME and RAE results of the four methods across different sequences. Tables 4 and 5 display the TPR and FAR values of the four algorithms in each test sequence. Table 6 shows the average values of the four evaluation metrics for the given sequences  
The algorithm proposed in this paper often results in lower grayscale values for pixels on the target edge compared to the original higher grayscale values. Consequently, threshold segmentation may lead to the loss of the target edge. This observation is supported by the average misclassification error and the average relative foreground area error, which indicate that the proposed algorithm performs slightly worse than the ISSD algorithm in terms of these two evaluation metrics. However, the differences between the specific data of the proposed algorithm and the ISSD algorithm are not substantial. Moreover, the proposed algorithm outperforms the other two comparison algorithms in terms of both the average misclassification error and the average relative foreground area error in most of the test sequences. From the perspective of TPR and FAR, although the ISSD and SRISTAS methods have obtained higher detection rates, the false alarm rate is also relatively high. In contrast, the proposed algorithm in this paper ensures the highest TPR among almost all sequences while maintaining a lower FAR. This demonstrates the superiority of the proposed algorithm over the compared methods considered in this study. From Figure 14, we can observe the performance of the four algorithms on different scene sequences. The line charts in Figure 14a-d depict the ME, RAE, FAR, and TPR for each test scenario, respectively. Figure 14c clearly shows that the proposed algorithm achieves a low false alarm rate. This can be attributed to the adoption of a dual-channel segmentation