# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 4 Experiments
### Structure Comparative Experiments  
In this paper, we also experimented with the different backbone networks of the encoder. While keeping the baseline as DeepLabv3+, we replaced different backbones to compare the effect of different feature extraction networks in the spacecraft segmentation issue. We conducted experiments with Mobilenet-v2 [56], ResNet-50 [17], ResNet-101 [17], Xception-65 [13], Densenet121 [57], and DRN-D-54 [16]. The experimental results are shown in Table 5 below. Although Xception-65 performs well in some image segmentation tasks, its applicability to spacecraft objects is relatively poor due to noise and the gridding artifacts. Mobilenet-v2 is also a backbone using depthwise separation convolutions and achieved good results. ResNet-50 [D] and ResNet-101 [D] represent the dilated convolution versions of ResNet. ResNet-50 [D] and ResNet-101 [D] replace a small number of standard convolutional layers in the network with dilated convolutions with the dilation rate = 2 or 4. They increase the receptive field but do not cause serious gridding artifacts. ResNet-101 [D] achieved higher scores for the segmentation of Spacecraft A with a larger field of perception and deeper network depth. Meanwhile, because DRN-D-54 removes some residual connections, the focus on low-level features is not as good as ResNet-101 [D], so the segmentation effect of the network for Spacecraft A can be further improved when the decoder structure focuses on the low-level features. From the MIoU metric, ResNet-50 [D] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Spacecraft A** & **Spacecraft B** & **Spacecraft C** & **MIoU** \\ \hline U-Net [15] & 64.42 & 83.51 & 90.13 & 79.35 \\ HRNet [55] & 40.04 & 32.59 & 56.9 & 43.18 \\ DeepLabv3+ [13] & 83.15 & 81.26 & 88.43 & 84.28 \\ PSPNet [43] & 83.61 & 81.88 & 93.84 & 86.44 \\ EMANet [53] & 74.38 & 89.12 & 94.18 & 85.89 \\ \hline Ours & 93.11 & 87.62 & 92.87 & 91.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The MIoU results of network comparative experiments on the test set. and ResNet-101 [D] show that the network adds dilation convolution and demonstrate that dilation convolution is still a boost for the spacecraft segmentation task. To achieve higher segmentation results, we have to pay attention to maintaining the depth of the network and enhancing the contextual information while avoiding the gridding artifacts. Therefore, we adopted the encoder structure based on DRN-D-54. Experimental results show that our encoder has a good segmentation effect on all objects while achieving the highest MIoU. Based on the above encoder experiments, while keeping the backbone network as DRN-D-54, we conducted experiments of adding a second ASPP module to the decoder structure. We let the low-level features in the baseline method directly enter the second ASPP module and then fuse with the high-level features passed through the ASPP module. The low-level features further aggregate contextual information through this process. During the experiments, we set different dilation rates for the second ASPP module. We achieved better segmentation results with fewer layers and larger dilation rates, as shown in Table 6 below. In the second experiment, we added a second ASPP structure that is identical to the first ASPP module. The segmentation results have been improved compared to the first experiment without adding the second ASPP module. However, in the third experiment, we removed the convolutional layer with the rate of 18, and the effect decreased instead because the low-level features require a larger receptive field to help improve the overall network effect. Therefore, we adjusted the convolutional layers in the parallel structure, as shown in the fourth experiment. We increased the dilation rate of convolution with all three layers only, which further improved the segmentation results.