结合轻量双注意力模块的孪生卷积网络变化检测

Siamese convolution network combined with lightweight dual attention modules for change detection

针对向神经网络模型中引入注意力模块,导致模型复杂度进一步提高的问题,该文提出了结合轻量双注意力模块的孪生卷积网络变化检测方法。通过在孪生卷积网络模型基础上,加入构建的轻量双注意力模块,达到了向模型中引入较少参数量并且提升变化检测性能的效果。在现有的两个公开数据集上,该文算法分别达到了95.2%和98.4%的总体检测精度,优于其他现有方法。

In order to tackle the problem that the introduction of attention modules would increase the complexity of the neural network,a Siamese convolution network combined with lightweight dual attention modules was proposed for change detection.The lightweight dual attention module could improve the accuracy of change detection with less parameters added to network.The overall accuracy of the proposed method reached 95.2%and 98.4%,respectively,on two public change detection datasets,and it indicated the effectiveness of our method.