Debris flow gully recognition based on residual attention mechanism

For debris flow disasters in valleys image classification problems, this paper improved the Resnet18 network, an improved convolution neural network model is put forward, through adding residual attention in network structure module, solved the original model to extract the image features to solve the problem of poor, edge model accurately capture the debris flow disasters in valleys in the image contour and internal ridge information.In addition, this paper also conducts comparative experiments on various attention mechanism structures, analyzes their differences, and obtains the attention mechanism network most suitable for debris flow disaster gully data.The experimental results show that the classification accuracy of the improved network model in debris flow disaster gullies reaches 75.42%, and its classification performance is improved by 5.1% compared with the Resnet18 network model.