# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 2 Related Work  
In 2016, [PERSON] et al. [9] proposed the YOLOv1 network. This network significantly improved real-time performance and accuracy by unifying the object detection task into a regression problem. Since then, the YOLO series of algorithms have undergone multiple iterations and improvements, becoming a significant presence in the field of object detection.  
The innovation of the YOLOv1 network lies in the integration of the object detection task into a single feedforward neural network. In contrast to traditional two-stage detection methods, YOLOv1 adopts an end-to-end approach by simultaneously predicting the bounding box coordinates and class probabilities of the targets within a single network. This not only enhances the real-time performance of object detection but also simplifies the entire detection process. However, YOLOv1 encountered challenges in detecting small objects and background misclassifications, which became focal points for improvement in subsequent versions. To address the issues present in YOLOv1, YOLOv2 [10] introduced the concept of anchor boxes. These predefined bounding box sizes help to better adapt to targets of varying sizes and proportions. Additionally, YOLOv2 adopted a deeper Darknet-19 network architecture and supported multi-scale predictions, enabling the algorithm to better accommodate targets of different sizes. YOLOv3 [11] further improved upon YOLOv2. Firstly, it adopted the deeper Darknet-53 network, enhancing the capability of feature extraction. Secondly, it introduced multi-scale predictions by performing object detection at different levels, thereby improving the algorithm's adaptability to targets of varying sizes. Additionally, YOLOv3 utilized smaller-sized anchor boxes, significantly enhancing the detection performance for small objects. YOLOv4 [12] represents another significant advancement in the YOLO series, introducing a range of new modules to further enhance performance. The introduction of the cross stage partial network [13] structure optimized feature fusion, the spatial attention module [14] strengthened attention to spatial information, and the path aggregation network contributed to better aggregation of features from different levels. Ref. [15] proposed YOLOv in 2021, consisting of seven versions. Here, we will primarily focus on the improvements made in YOLOv-Darknet53. In the input section, data augmentation is achieved using mosaic and mixup techniques. The neck section employs a feature pyramid network (FPN) for feature fusion. In the prediction section, three decoupled heads are used to enhance accuracy and expedite convergence. The model adopts anchor-free methods to reduce the parameter count. During label assignment, positive samples are initially filtered based on the center point and target box. Subsequently, fine filtering is performed using SimOTA. The loss function is then utilized to calculate the discrepancy between the target box and the predicted box for positive samples.  
In addition to major updates in YOLO, many researchers have delved into the study of each version of YOLO. [PERSON] et al. [16] replaced the backbone network of the YOLOv5 network with the GhostConv network. This network successfully reduced the number of network parameters by half without reducing the accuracy of target detection. [PERSON] et al. [17] merged YOLOv8 with context-guided network and residual network (ResNet) structures with multiple branches to enhance the model's ability to learn deep ResNet features. [PERSON] et al. [18] replaced the original YOLOv5 backbone network with the lightweight ShuffleNetv2 to reduce parameters and computational requirements. Additionally, they integrated BiFPN as the feature fusion layer, enhancing the model's detection capability across various object scales. Furthermore, they employed a CARAFE lightweight upsampling factor to improve the model's perception of details and small-sized objects and the EIOU loss function to expedite model convergence.