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

Abstract

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 F1-score (mF1) on the multi-category bridge pier defect dataset, exhibiting state-of-the-art (SOTA) performance, and has global modelling 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.