目标检测算法——YOLOv5结合ECA注意力机制

论文题目：《ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks》
论文地址：  https://arxiv.org/pdf/1910.03151.pdf 

实验证明，将即插即用的ECA注意力模块嵌入到YOLOv5网络中，减少模型参数，同时带来明显的性能提升。

1.网络结构图

2.ECA模块代码

# class eca_layer(nn.Module):
#     """Constructs a ECA module.
#     Args:
#         channel: Number of channels of the input feature map
#         k_size: Adaptive selection of kernel size
#     """
#     def __init__(self, channel, k_size=3):
#         super(eca_layer, self).__init__()
#         self.avg_pool = nn.AdaptiveAvgPool2d(1)
#         self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
#         self.sigmoid = nn.Sigmoid()
#
#     def forward(self, x):
#         # feature descriptor on the global spatial information
#         y = self.avg_pool(x)
#
#         # Two different branches of ECA module
#         y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
#
#         # Multi-scale information fusion
#         y = self.sigmoid(y)
#         x=x*y.expand_as(x)
#
#         return x * y.expand_as(x)

如何嵌入YOLOv5网络，各位小伙伴请参考CBAM那篇博文~