The segmentation of debris-flow fans based on local features and spatial attention mechanism

In response to issues such as incomplete segmentation and the presence of breakpoints encountered in extracting debris-flow fans using semantic segmentation models, this paper proposes a local feature and spatial attention mechanism to achieve precise segmentation of debris-flow fans. Firstly, leveraging the spatial inhibition mechanism from neuroscience theory as a foundation, an energy function for the local feature and spatial attention mechanism is formulated. Subsequently, by employing optimization theory, a closed-form solution for the energy function is derived, which ensures the lightweight nature of the proposed attention mechanism algorithm. Finally, the performance of this algorithm is compared with other mainstream attention mechanism algorithms embedded in semantic segmentation models through comparative experiments. Experimental results demonstrate that the proposed method outperforms both the original models and mainstream attention mechanisms across various classic models, effectively enhancing the performance of network models in debris-flow fan segmentation tasks.