SE注意机制提高检测精度的过程： 本实验在不改变YOLOv5原有的backbone主干网络的基础上在末尾添加SE注意力机制模块这个模块包括一个全局平均池化层和两个全连接层用于计算每个通道的权重。在训练过程中SE模块会自适应地学习每个通道的权重使重要通道得到的注意力有效提高；在测试过程中SE模块会根据学习到的权重对每个通道的特征进行加权平均得到最终的特征表示。通过Yolov5同SE注意力机制融合YO

In this experiment, we aimed to improve the detection accuracy of YOLOv5 by incorporating the SE attention mechanism without altering the backbone network. The SE module consists of a global average pooling layer and two fully connected layers that compute the weight of each channel. During training, the SE module adaptsively learns the weight of each channel to effectively enhance the importance of crucial channels. During testing, the SE module weights each channel's feature representation according to the learned weights and obtains the final feature representation. By fusing the YOLOv5 with the SE attention mechanism, the YOLOv5s network can better adapt to different datasets and scenarios, thereby improving detection accuracy.