Resumen de The Segmentation of Debris Flow Fans Based on Spatial Coordinate Attention Mechanism
Xin Tong, Baojun Wang
In response to the low accuracy and poor performance of traditional machine learning methods in identifying debris flow fans. This paper proposes an optimized Simple, Parameter-Free Attention Module (SimAM) attention mechanism named Spatial Coordinate Attention Module. It combines with convolutional neural networks to achieve precise segmentation of debris flow fans. Firstly, the energy function of the SimAm is improved to retain the spatial coordinate information of features. Secondly, the closed-form solution of the module is obtained through optimization theory to ensure lightweightness, resulting in the Spatial Coordinate Attention Module. Finally, the Spatial Coordinate Attention Module is embedded into classic segmentation network models to compare with mainstream attention mechanisms. Experimental results demonstrate that the proposed method outperforms mainstream attention mechanisms in various classic models, yielding more complete segmentation results. This approach effectively enhances the segmentation performance of the network models in the task of debris flow fans segmentation.
