在文章中的修改： 本文基于YOLOv5s融合SE注意力机制研究反光衣检测具体实验结果如表3所示。Yolov5s是一种基于深度学习的目标检测模型它通过融合SE注意力机制从而提高模型在目标检测任务中的准确度和效率。SE注意力机制可以应用于各种类型的深度学习模型中其基本思想是根据每个通道的重要性来自适应地调整通道的权重。具体来讲Yolov5s将SE注意力机制嵌入到卷积层中通过对每个通道进行缩放来调整特征

In this study, we explored the use of YOLOv5s with SE attention mechanism for reflective vest detection. The experimental results, as shown in Table 3, indicated that by incorporating SE attention mechanism, YOLOv5s achieved improved accuracy and efficiency in object detection tasks. SE attention mechanism is a powerful tool that can be applied to various types of deep learning models. Its fundamental principle is to adaptively adjust the weights of each channel based on their importance. Specifically, YOLOv5s embedded SE attention mechanism into the convolutional layer, allowing for the scaling of each channel to adjust the weight of each channel in the feature map. During training, the SE attention mechanism automatically learns the weight of each channel, which improves the model's ability to detect objects and enhances the accuracy of object detection. Therefore, it can be concluded that the addition of SE attention mechanism to YOLOv5 significantly improves the accuracy of object detection to a certain extent.