Abstract
Abstract In recent years, Convolutional Neural Networks (CNNs) have become an important research direction in the field of building damage assessment. Particularly, deep neural networks based on the U-shaped architecture and skip connections have achieved significant breakthroughs in the task of architectural damage assessment. Despite the impressive performance of CNNs, effectively capturing global and long-range semantic information remains a challenge due to the local nature of their convolutional operations. To address this issue, we propose a novel architectural damage assessment model called Bi-DAUnet, which adopts a BiFormer structure similar to U-Net. In this model, we employ a U-shaped encoder-decoder architecture based on BiFormer and combine it with skip connections to achieve global semantic feature learning. Specifically, we utilize a hierarchical BiFormer with a dual-layer routing attention mechanism as the encoder to extract contextual features of architectural images. In the symmetric decoder, a BiFormer Block is introduced to fuse shallow and deep features of the feature maps and learn the correlation between pixels at distant locations. Experimental results indicate that the U-shaped encoder-decoder network based on BiFormer achieves superior performance in the task of architectural damage assessment compared to fully convolutional methods.
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