# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 1 Introduction  
With rapid urbanization and the continuous development of architectural structures, elevators, as indispensable modes of transportation in modern society, play a crucial role in connecting various floors. However, in the day-to-day functioning of elevator systems, ensuring passenger safety and service quality has consistently been a crucial concern necessitating attention and improvement. Object detection technology within elevators, as an essential means to enhance elevator operational efficiency and ensure passenger safety, has garnered widespread research and application attention [1; 2]. Due to the rapid advancement of computer vision and deep learning technologies, object detection techniques have found widespread applications in diverse fields. However, object detection within elevators still faces a series of challenges compared to other fields due to the unique environment and complex scenes. Detecting objects in elevator scenes presents several challenges. Firstly, elevators typically have complex backgrounds, including walls, floors, and ceilings, which can interfere with accurate object detection. Secondly, lighting conditions may vary from dim to bright, impacting algorithm performance. Moreover,during peak elevator usage, there can be a high density of objects, leading to overlapping targets and increased difficulty in identification [3,4]. Therefore, addressing these challenges requires comprehensive consideration and tailored algorithm design to enhance accuracy and stability in object detection within elevator environments. With the rise of deep learning, object detection algorithms based on deep learning have become mainstream. These algorithms leverage neural networks for end-to-end learning and processing. Object detection algorithms based on deep learning can be broadly classified into two categories: two-stage detection algorithms and one-stage detection algorithms. Two-stage algorithms, exemplified by the region-based convolutional neural network (R-CNN) series [5], were among the earliest object detection algorithms based on deep learning. R-CNN utilizes selective search to generate candidate regions, followed by convolutional neural network feature extraction for each region and, finally, employs a classifier for object detection. Fast R-CNN [6] optimized R-CNN by introducing a region of interest pooling layer, thereby achieving higher computational efficiency. Faster R-CNN [7] further introduced the region proposal network, enabling end-to-end learning in object detection. While these methods exhibit high detection accuracy, their detection speed is relatively slow, resulting in reduced efficiency. One-stage algorithms, exemplified by the Single Shot Multibox Detector (SSD) and the You Only Look Once (YOLO) network, adopt a different approach. SSD [8] predicts target boxes and categories at different scales on feature maps of various levels. This design enables SSD to achieve a faster detection speed at the potential expense of some detection accuracy. YOLO [9] is another one-stage algorithm that divides the image into a grid and performs object detection directly on each grid, outputting bounding boxes and categories. The YOLO series also exhibits fast detection speed but with relatively lower detection accuracy. In summary, deep learning algorithms have made significant progress in the field of object detection. Nevertheless, these algorithms are continuously evolving and improving to strike a balance between detection accuracy, speed, and efficiency. This paper utilizes the YOLOv8 network as the baseline model for implementing the elevator passenger detection task and proposes improvements upon the YOLOv8 network. The main contributions of this study are as follows:  
1. An SCConv improved ConvModule (SC-ConvModule) is proposed by incorporating a spatial and channel reconstruction convolution (SCConv) onto the ConvModule in the YOLOv8 backbone network.