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Efficiently monitoring the condition of civil infrastructure requiresautomating the structural condition assessment in visual inspection. This paperproposes an Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) forautomatic structural condition assessment in visual bridge inspection.AECIF-Net can simultaneously parse structural elements and segment surfacedefects on the elements in inspection images. It integrates two task-specificrelearning subnets to extract task-specific features from an overall featureembedding. A co-interactive feature fusion module further captures the spatialcorrelation and facilitates information sharing between tasks. Experimentalresults demonstrate that the proposed AECIF-Net outperforms the currentstate-of-the-art approaches, achieving promising performance with 92.11% mIoUfor element segmentation and 87.16% mIoU for corrosion segmentation on the testset of the new benchmark dataset Steel Bridge Condition Inspection Visual(SBCIV). An ablation study verifies the merits of the designs for AECIF-Net,and a case study demonstrates its capability to automate structural conditionassessment.

Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection