# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 5 Conclusions  
To achieve precise detection of passengers in elevator scenarios, this paper proposes an SC-YOLOv8 object detection network incorporating soft-pooling and attention mechanisms. By replacing the ConvModule in the YOLOv8 backbone network with SCConv, this network aims to reduce spatial and channel redundancy among features in the convolutional neural network, leading to model compression and enhancing its performance. Additionally, addressing the potential loss of partial local information in the YOLOv8 network's SPP-Fast module due to relying solely on max-pooling operations, this paper introduces a soft-pooling branch to construct a dual-branch SPP-Fast module. Furthermore, this research embeds the proposed soft-pooling and multi-scale convolution CBAM in the YOLOv8 network, improving the CBAM module by using multi-scale depthwise separable convolution kernels and soft-pooling technology.  
Finally, this paper established an elevator passenger dataset and utilized data augmentation techniques to preprocess the dataset. The experiments in this paper were conducted on an elevator passenger dataset, and the results demonstrate that the proposed improved YOLOv8 network achieved an accuracy of 94.32%. The recall rate and mAP (mean average precision) values were 91.17% and 92.95%, respectively. In terms of the \"Params\" and \"FLOPs\" metrics, the improved YOLOv8 network values proposed in this paper are 50.4 M and 174.6 B, respectively. These metrics are not significantly increased compared to the YOLOv8 network. In conclusion, the SC-YOLOv8 network based on soft-pooling and attention shows excellent performance in the elevator scenario, providing an effective solution for accurate detection of elevator passengers.  
Data curation, D.F.; funding acquisition, J.C., P.Y., B.F. and D.F.; investigation, [PERSON] and J.C.; methodology, Z.W. and J.C.; project administration, P.Y.; resources, J.C. and P.Y.; software, Z.W.; validation, P.Y.; visualization, B.F.; writing--original draft, Z.W.; writing--review and editing, J.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Params (M)** & **FLOPs (B)** \\ \hline YOLOv8 & 48.1 & 169.2 \\ Ours & 50.4 & 174.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison experiment of Params and FLOPs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mAP (\%)** \\ \hline Faster-RCNN & 82.87 \\ YOLOv3 & 77.26 \\ YOLOv5 & 81.29 \\ YOLOv8 & 83.83 \\ Ours & 85.12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative experiments on the CrowdHuman dataset.  
**Funding:** This work was substantially supported by the Scientific Research Plan Project of the Zhejiang Provincial Market Supervision Administration:ZC2023080, the Scientific Research Plan Project of the Zhejiang Provincial Market Supervision Administration: ZC2021B075, and the Key Research and Development Program Project of the Huzhou Science and Technology Bureau: 2023 ZD2017.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are not publicly available due to privacy.  
**Conflicts of Interest:** [PERSON] was employed by the company Su Xun Elevator Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.