Automated Intelligent Detection of Defects on Bridge Piers Using a Climbing Operation Robot and Vision Mamba

Superficial defect detection on bridge piers is crucial for assessing the health condition of bridges. A novel ring vision acquisition system based on a climbing operation robot is proposed to aim at the limitations of existing unmanned aerial vehicles (UAVs) and wall-climbing robots. In addition, t...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 74; pp. 1 - 18
Main Authors: Du, Hao, Wang, Hui-Feng, Shan, Yuan-He, Pan, Ze-Feng, Jiao, Yun-Mei, Gao, Rong, Huang, He
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Automation
Bridge inspection
Bridge piers
Bridges
climbing robot
Climbing robots
Complexity
Computational modeling
Defect detection
Defects
defects detection
Image acquisition
Image resolution
Inspection
linear computational complexity
Piers
Rails
Robots
Safety
state space model
Transformers
Unmanned aerial vehicles
Vision
Visualization
ISSN:0018-9456, 1557-9662
Online Access:Get full text
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Summary:Superficial defect detection on bridge piers is crucial for assessing the health condition of bridges. A novel ring vision acquisition system based on a climbing operation robot is proposed to aim at the limitations of existing unmanned aerial vehicles (UAVs) and wall-climbing robots. In addition, to address the quadratic computational complexity of the Transformer-based model, a Vision Mamba-based defects detection model, piers defects-Mamba (PD-Mamba), is proposed, which contributes to more efficient inferencing the high-resolution defects images. Finally, field experiments were performed at Shouchun Bridge in Anhui Province, China. The results demonstrated that the robotic system could automate acquiring high-precision visual images of bridge piers' superficial defects. The proposed PD-Mamba achieves 77.97% mean intersection over union (mIoU), 88.81% mean precision (mP), 85.42% mean recall (mR), and 87.05% mean <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula>-score (m<inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula>) on the multicategory bridge pier defect dataset, exhibiting state-of-the-art (SOTA) performance, and has global modeling capability and linear computational complexity, which achieves accurate and efficient detection of bridge piers' superficial defects. It is important to assess the health condition of the bridge.
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ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3577835