# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 4 Experiments
### Experiment Results  
This paper trained the YOLOv8 network and the improved YOLOv8 network on the elevator passenger dataset. The performance of these models was compared through training curves. The red curve in Figure 17 represents the training process of the original YOLOv8 network, while the blue curve represents the model after the proposed improvements. After the initial 75 training epochs, the performance metrics of the improved model were consistently superior to those of the original YOLOv8 model. This indicates that the enhanced network structure can learn task features more quickly and effectively within the same number of training epochs, achieving better performance. As shown in Figure 18, the loss curve during the training process of the improved YOLOv8 network indicates that, in the initial stages of model training, the improved model demonstrates higher learning efficiency with a rapid decrease in the loss curve. As training progresses, the slope of the loss curve gradually decreases, and around 200 training iterations, the model's learning efficiency tends to saturate. At this point, the loss curve fluctuate slightly around 0.001. Ultimately, the loss curves during training and validation exhibit a stable and accompanying state, indicating that the improved YOLOv8 network demonstrates relatively good performance in terms of parameter adjustment and generalization capabilities. This is particularly beneficial for object detection tasks in practical applications where robustness and precision are crucial. Overall, the proposed improvement strategy effectively enhances the training and generalization performance of the YOLOv8 model. This paper conducted a comparative analysis of the detection performance of the improved YOLOv8 network against advanced object detection networks, such as Faster-RCNN, YOLOv3, YOLOv5, and YOLOv8. The results are presented in Table 3, and the experimental findings indicate that the proposed improved YOLOv8 network achieves a precision of 94.32%. This represents an improvement of 3.9%, 5.18%, 3.96%, and 2.55% over Faster-RCNN, YOLOv3, YOLOv5, and YOLOv8, respectively. The recall and mAP values are 92.17% and 92.95%. Figure 17: Training results for YOLOv8 and our network. Figure 18: Loss curve. Figures 19 and 20 present visualizations of the partial detection results during the testing process. In some selected samples, mutual occlusion among passengers is observed. Both the original YOLOv8 network and the improved YOLOv8 network proposed in this paper accurately regress the bounding boxes of passengers inside the elevator cabin when there is no occlusion, with no instances of missed detections, as shown in the first and second image of the first row. Upon closer inspection, it becomes apparent that the proposed improved network demonstrates more accurate detection under conditions of moderate occlusion, as illustrated in the third image in the first row. In scenarios of severe occlusion and blur, as shown in the first and second image of the second row, the original YOLOv8 network may fail to detect passengers, while the algorithm proposed in this paper continues to perform well in detecting passengers even under heavy occlusion. In summary, applying the proposed improved YOLOv8 network for accurate multi-object detection of passengers in elevator scenarios is feasible. This lays a solid foundation for subsequent tasks, such as passenger tracking and anomaly behavior recognition within elevator cabins. This paper conducted ablation experiments to investigate the contributions of different improvement modules to the performance of the enhanced network model. The results are presented in Table 4. From the experimental findings, it can be observed that introducing the SCConv and the dual-branch SPP-Fast structures into the original YOLOv8 architecture led to certain precision improvements, with increases of 1.09% and 0.32%. Notably, incorporating attention mechanisms resulted in a significant precision improvement, with a gain of 1.14%.