# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 2 Space Debris Detection
### Spatial Feature Extraction  
Many high-performance feature extractors, such as ResNet [40], Xception [41], etc., have been proposed in recent years. However, these models possess massive model parameters (typically tens of millions) and high floating-point operations (FLOPs), which are not well-suited to be deployed on current space-based platforms with limited memory and computational resources [42]. Accordingly, MobileNetV3 [43], one of the most popular lightweight backbones, is modified as the spatial features extraction module. MobileNetV3 has fewer parameters, lower FLOPs, and faster inference speeds. Moreover, it is well-matched for low resource-use cases and achieves a high accuracy-latency trade-off with mobile devices. The proposed spatial feature extraction network is shown in Figure 3. It mainly consists of a convolutional layer with a kernel size of 3 \(\times\) 3 and a series of bottleneck structures [43]. The bottleneck structure is a resource-efficient block composed of an inverted residual structure and linear bottleneck layers with squeeze and excitation modules [44]. The inverted residual structure could improve the ability of a gradient to propagate across multiplier layers as well as allow for considerable memory-efficient implementation. The linear bottleneck layers could prevent the information loss caused by non-linear transformations, such as rectified linear unit (ReLU) activation. The squeeze and excitation module is a channel-wise attention module applied in the linear bottleneck layers to focus on the most important feature representations.  
Figure 2: Detailed illustration of the proposed saliency detection network, which consists of the spatial feature extraction module, spatial feature enhancement module, temporal feature extraction module, and saliency prediction module.  
Compared to the original MobileNetV3 model, three modifications were performed to make it applicable for space debris feature extraction.  
1. Some layers of the original model are tailored to further reduce the model parameters to adapt to space-based applications. Specifically, the last three bottleneck blocks were removed. This is because the space debris in the satellite video frame had a simple structure and small size, which did not need a deeper network to extract complex semantic features or a larger receptive field to detect the small objects.  
2. The SFM was divided into five blocks and five different resolutions of feature maps were outputted correspondingly. The feature maps from shallow layers with higher resolutions preserved detailed features, such as position and intensity, which are beneficial for small object feature extraction. The high-level features with large receptive fields contain rich semantic features such as contours, which are important for learning temporal cues and distinguishing salient objects [45]. Based on the above facts, we used the first four blocks to extract multi-scale spatial features and the last block to learn temporal features.  
3. A lightweight atrous spatial pyramid pooling (LR-ASPP) [43] module was added to the last block, which could capture the image-level global context features and improve the inference speed.  
Figure 3 shows the first five outputted feature maps of each block, which have half the input resolution of the previous block. We can see that the first two blocks respond to detailed corners and color conjunctions. Block 3 only outputs more coarse color and position information. The last two blocks almost don't extract any semantic features. Therefore, high-level features should be strengthened for spatial-temporal feature learning.