# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 4 Experiments and Analysis
### Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network** & **AP** & **AP\({}_{50}\)** & **AP\({}_{75}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **mAP** \\ \hline Inception-v3 & 90.6 & 96.3 & 92.2 & - & 79.7 & 93.3 & 90.42 \\ ResNet-50 & 87.9 & 97.0 & 89.9 & - & 72.1 & 91.7 & 87.72 \\ ResNet-100 & 85.4 & 95.4 & 89.3 & - & 69.1 & 89.4 & 85.72 \\ ELAN-based & 91.2 & 97.2 & 92.3 & - & 82.4 & 93.3 & 91.28 \\ ELAN+SimAM & 91.3 & 97.0 & 93.2 & - & 81.4 & 93.6 & 91.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of backbone and attention module for instance-segmentation network. **Attention module. ** This paper compares the effect of adding SimAM to the network using an ELAN-based backbone. For only always instance-segmentation network, the mAP is slightly larger when SimAM is added, as shown in Table 3. As expected, adding SimAM also improves our novel network with the instance-segmentation branch and skeleton-detection branch, as shown in Table 4. However, it performs poorly for the network for skeleton detection, as shown in Table 5.  
**Loss function parameter. ** This paper designs a novel fused loss function to adjust the dynamic weight of the two branches during training. To increase the weight of the skeleton-detection branch as the loss of the instance-segmentation branch gradually stabilizes, the sigmoid function is chosen as the base. The sum of the weights for the two branches is always kept at one. This paper adds two parameters to adjust the function: alpha  
This paper varies \(\beta^{\prime}\) from 1 to 18 while keeping \(\alpha^{\prime}\) fixed at 1, and the curves for each value are shown in Figure 8c. When \(\beta^{\prime}\) is equal to one, the overall rate of weight increases changes the slowest during training. A new mean metric was calculated by assigning the weights of 0.35 to AP and AR, and 0.3 to F-score. It is mainly because the instance-segmentation branch is more important at the beginning of training. As shown in Figure 8d, the best result is obtained when the \(\beta^{\prime}\) is set to one. The value of \(\alpha^{\prime}\) was changed from 0.5 to 1 while keeping \(\beta^{\prime}\) fixed at its best value, and the curves for each value are shown in Figure 8a. Decreasing \(alpha^{\prime}\) means the weight of the skeleton-detection branch is lower during the same training epoch. The mean metrics is calculated in the same way as evaluating \(\beta^{\prime}\). As shown in Figure 8b, the best value for \(\alpha^{\prime}\) is 0.9. **Parallel network. ** The main idea behind our network is to add a parallel instance-segmentation branch to remove the skeleton of the wrong target, on the base of the skeleton-detection network. This paper compares the results of our novel network to the skeleton-detection network. As shown in Table 6, our network achieves a 3.1% improvement in F-score compared to the skeleton detection network with same backbone and attention model. It is a 2.2% improvement compared to the fixed AdaLSN with the original backbone. As expected, the results of the skeleton-detection network show skeletons of objects that are not railways, which are not present in our novel parallel network shown in Figure 9.