1. Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automated structural condition assessment in visual inspection.
- Author
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Zhang, Chenyu, Yin, Zhaozheng, and Qin, Ruwen
- Subjects
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INFRASTRUCTURE (Economics) , *SURFACE defects , *BRIDGE inspection , *ABLATION (Industry) , *DEEP learning , *INSPECTION & review , *IRON & steel bridges , *INFORMATION sharing - Abstract
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. • A deep learning model AECIF-Net is developed for automating the visual assessment of structural conditions. • SBCIV, an image dataset with annotations of structural elements and their surface defects, is created. • AECIF-Net outperforms current methods in segmenting structural elements and surface defects. • Merits of the designs for the AECIF-Net are verified by comprehensive experiments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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