  Efficiently monitoring the condition of civil infrastructure requires
automating the structural condition assessment in visual inspection. This paper
proposes an Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for
automatic structural condition assessment in visual bridge inspection.
AECIF-Net can simultaneously parse structural elements and segment surface
defects on the elements in inspection images. It integrates two task-specific
relearning subnets to extract task-specific features from an overall feature
embedding. A co-interactive feature fusion module further captures the spatial
correlation and facilitates information sharing between tasks. Experimental
results demonstrate that the proposed AECIF-Net outperforms the current
state-of-the-art approaches, achieving promising performance with 92.11% mIoU
for element segmentation and 87.16% mIoU for corrosion segmentation on the test
set 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 condition
assessment.
