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图中每个通道的权重

In this study, we explore the detection of reflective clothing using YOLOv5s with SE attention mechanism. The experimental results are shown in Table 3. YOLOv5s is a deep learning-based object detection model that improves the accuracy and efficiency of the model in object detection tasks by integrating the SE attention mechanism. The SE attention mechanism can be applied to various types of deep learning models, and its basic idea is to adaptively adjust the weights of channels based on their importance. Specifically, YOLOv5s embeds the SE attention mechanism into the convolutional layer and adjusts the weight of each channel in the feature map by scaling. During the training process, the SE attention mechanism automatically learns the weight of each channel, enabling the model to better detect the target and improve the accuracy of object detection. Thus, it can be concluded that the detection accuracy of YOLOv5 is significantly improved to some extent after incorporating the SE attention mechanism.