# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 3 Method
### Lightweight LP-YOLO Model
#### 3.2.1 Backbone  
Given that the original YOLOv5 model utilizes CSPDarkNet as its backbone for feature extraction, which comprises numerous regular convolutional layers and demands significant computational resources, we propose a lightweight network called PartitionNet to supplant CSPDarkNet. The Partition module is shown in Figure 2b. Firstly, we make a comparison with the Ghost module and simplify the Ghost module in Figure 2a, where C1 and C2 represent the quantity of the input channels and output channels, respectively. The Ghost module aims to reduce the number of \(1\times 1\) convolutional kernels by half during the convolutional operations, followed by an inexpensive operation that generates a set of transformed features. The remaining half of the feature maps and transformed features are concatenated to generate output features, thereby reducing the number of nonlinear operations and significantly diminishing the computational resources and parameters. However, the intrinsic features are just produced by half of pointwise convolution, and the spatial information is only captured by inexpensive operations with depthwise convolution. This method may lead to weak spatial information capturing, which could hinder detection performance. To address this challenge, we propose a module in a partitioned way, named the Partition module. It consists of three components: pointwise convolution, max-pooling layer, and depthwise convolution. We set a kernel size of 3 and a stride of 1 in the max-pooling layer, designed to eliminate redundant information and enhance the robustness of extracted features. In order to avoid feature compression and make the subsequent concatenation convenient, we set a padding of 1. Besides, we leveraged the \(3\times 3\) depthwise convolution and \(1\times 1\) convolution to decrease the parameters in our network. More importantly, we separated the C2 into four parts, with a C2 ratio of \(1\times 1\) convolution, a max-pooling layer, and \(3\times 3\) depthwise convolution being 1, 1, and 2, respectively. This allowed us to use only a quarter of the convolutional kernels to produce features instead of half of the Ghost module and adding the max-pooling layer to alleviate the information loss caused by direct splicing with the original feature map. Through this approach, we alleviated the computational burden and achieved improved performance, as demonstrated by our experiments. Figure 1: Framework of our proposed LP-YOLO. The backbone is PartitionNet; the neck is the new path aggregation network. The VH block is proposed to enhance information flow after the down-sampling layer. Inspired by MobileNetV2, our Partition block is also an inverted bottleneck. It is shown in Figure 2c and composed of two Partition modules stacked in series. The first Partition module functions as an amplification layer to augment the number of channels, while the second one squeezes the output channels to align with the residual connection. We incorporated a down-sampling function utilizing a \(3\times 3\) depthwise convolution with a stride of 2. In addition, we utilized the sigmoid-weighted linear unit (SiLU) [37] activate function to introduce nonlinearity into the network to enhance the network's generalization ability. Compared to ReLU, SiLU has self-stability and absorbs the advantages of ReLU, which is smoother around zero so that negative values also have slight activation, and at the same time, it plays a balancing role on the gradient with a larger value. The formula of SiLU is defined as:  
\[SiLU(x)=x\cdot sig(x) \tag{1}\]  
where \(sig\) and \(x\) are represented as the sigmoid function and input value, respectively. Finally, the backbone of LP-YOLO is constructed by stacking the Partition block and VH block, named PartitionNet. The specific details are provided in Table 1, which contains six columns.