Attention mechanism and lightweight network fusion HRNet: a lightweight remote sensing road extraction algorithm integrating attention mechanisms

As the application of urban road extraction becomes more widespread, the challenges of segmentation errors and embedding large models into hardware become more complex. To solve these problems, an algorithm called attention mechanism and lightweight network fusion high-resolution network (AMLN-HRNet) is proposed. The network includes a lightweight convolution called deep sparse channel and spatial encoding convolution (DSCEConv) for road extraction, which greatly reduces the number of parameters in the model. To make the extraction of roads more accurate, an attention mechanism called lightweight dynamic weighted is designed. In addition, a parameterless attention mechanism is introduced to make the model properly combine the spatial correlation and topological structure of the road to improve extraction accuracy. Through experimental results, AMLN-HRNet can effectively balance the speed and accuracy of the model.