# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 3 Method
### SC-ConvModule  
The SC-ConvModule consists of a sequential combination of SCConv, the batch normalization layer, and the sigmoid linear unit (SiLU) activation function, as depicted in Figure 5.  
SCConv reduces the storage space and computational cost of the YOLOv8 network by reducing the spatial and channel redundancy between features in the convolutional neural network, while improving the accuracy and generalization of the YOLOv8 network for object detection tasks. SC-ConvModule further improves the stability and convergence speed of the network through the batch normalization layer. Batch normalization improves the stability of model training by performing a normalization operation on the inputs of each batch, i.e., each input is subtracted from the mean of the inputs of that batch and divided by the standard deviation, which keeps the distribution of inputs of each layer of the network small, helps prevent excessive growth or reduction in the gradient, and improves the stability of model training. In addition, batch normalization also reduces the sensitivity to the initialization parameters, making it easier for the neural network to converge to a suitable solution. SC-ConvModule introduces nonlinear factors through the SiLU activation function layer to enhance the expressive power of the network.The unboundedness and smoothing of the SiLU function prevents the problem of vanishing gradients in neural networks, allowing the network to be more flexible in adapting to different features. In addition, the non-monotonicity of the SiLU function enables it to handle more complex feature mappings, thus helping the network to learn more complex feature representations [22]. The CSP_2 Conv module is the result of an improvement to the C3 module of the YOLOv5 network, and the key to this improvement is the introduction of extended efficient layer aggregation networks (ELANs) to effectively avoid the problem of deterioration in the network's convergence during model scaling. As illustrated in Figure 6, the ELAN structure is a layer-aggregation architecture with an effective gradient propagation path [23]. It optimizes the gradient length of the entire network by utilizing a stack structure within the computation block. When the features are input into the CSP_2 Conv module, they first undergo a convolution operation for channel integration, and the integrated feature map has a richer representation. The CSP_2 Conv module incorporates parallel bottleneck layer structures, which diverge different dimensional feature map information and then merge them at the end of the module. Consequently, the CSP_2 Conv module can obtain richer gradient information, and the backpropagation gradients can be more effectively transmitted to shallower feature maps. This enhances the feature extraction capability of the convolutional neural network while reducing the time consumption of memory access. Figure 5: SC-ConvModule. In SC-ConvModule, the feature map first passes through an SCConv layer for feature extraction and transformation. Immediately after that, the BatchNorm submodule performs batch normalization on the output of the SCConv layer to adjust the distribution of the data, making the model training more stable and efficient. Subsequently, the normalized data enter the SiLU activation function to perform a nonlinear transformation to further increase the expressive power of the model. As the ConvModule is also present within the CSP_2 Conv module, it is replaced with the SC-ConvModule. The modified CSP_2 Conv module is referred to as the SC-CSP_2 Conv module, and its specific structure is illustrated in Figure 7.  
In YOLOv8, the bottleneck within the CSP_2 Conv module consists of two ConvModules and is augmented with a skip connection, as illustrated in Figure 8a. The primary function of the bottleneck is to propagate low-level feature information [24], enabling effective learning and convergence during the training process, even in the case of a deep  
Figure 6: Structure of ELAN. Figure 7: SC-CSP_2 Conv module.