Pipeline Multi-type High Consequence Area Identification Based On Mask R-cnn With Fused Attention Mechanisms

This article discusses the use of a CA attention mechanism in the Mask R-CNN model to optimize weight matrices and improve accuracy in identifying high-consequence areas in GIS. The research process includes data preprocessing, network modification, and validation through comparison and ablation experiments.