Automatic concrete dam defect image description generation method based on graph attention network

Abstract

An automatic concrete dam defect image description generation method based on graph attention network, including: 1) extract the local grid features and whole image features of the defect image and conduct image coding by using multi-layer convolutional neural network; 2) construct the grid feature interaction graph, and fuse and encode the grid visual features and global image features of the defect image; 3) update and optimize the global and local features through the graph attention network, and fully utilize the improved visual features for defect description. The invention constructs the grid feature interaction graph, updates the node information by using the graph attention network, and realizes the feature extraction task as the graph node classification task. The invention can capture the global image information of the defect image and the potential interaction of local grid features, and the generated description text can accurately and coherently describe the defect information.