Enhancing Bridge Deck Delamination Detection Based on Aerial Thermography Through Grayscale Morphologic Reconstruction: A Case Study
Environmental-induced temperature variations across the bridge deck were one of the major factors that degraded the performance of delamination detection through thermography. The non-uniformly distributed thermal background yields the assumption of most conventional quantitative methods used in practice such as global thresholding and k-means clustering. This study proposed a pre-processing method to estimate the thermal background through iterative grayscale morphologic reconstruction based on a pre-selected temperature contrast. After the estimation of the background, the thermal feature of delamination was kept in the residual image. A UAV-based nondestructive survey was carried out on an in-service bridge for a case study and two delamination quantization methods (threshold-based and clustering-based) were applied on both raw and residual thermal image. Results were compared and evaluated based on the hammer sounding test on the same bridge. The performance of detectability was noticeably improved while direct implementation of post-processing on raw image exhibited over- and under-estimation of delamination. The selection of pre-defined temperature contrast and stopping criterion of iteration were discussed. The study concluded the usefulness of the proposed method for the case study and further evaluation and parameter tuning are expected to generalize the method and procedure.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringQuantizationSimilar Papers 제목 키워드 기반
CNN-Based Deep Architecture for Reinforced Concrete Delamination Segmentation Through Thermography
Delamination assessment of the bridge deck plays a vital role for bridge health monitoring. Thermography as one of the nondestructive technologies for delamination detection has the advantage of efficient data acquisitio…
Image SegmentationSegmentationSemantic SegmentationData-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks
Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper prese…
Multi-class ClassificationMulti-Sensor Attention Networks for Automated Subsurface Delamination Detection in Concrete Bridge Decks
Subsurface delaminations in concrete bridge decks remain undetectable through conventional visual inspection, necessitating automated non-destructive evaluation methods. This work introduces a deep learning framework tha…
A Data Fusion Platform for Supporting Bridge Deck Condition Monitoring by Merging Aerial and Ground Inspection Imagery
UAVs showed great efficiency on scanning bridge decks surface by taking a single shot or through stitching a couple of overlaid still images. If potential surface deficits are identified through aerial images, subsequent…
SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic
This work focuses on using advanced techniques for structural health monitoring (SHM) for bridges with Traffic. We propose an approach using deep reinforcement learning (DRL)-based control for Unmanned Aerial Vehicle (UA…
Deep Reinforcement LearningEdge DetectionStructural Health MonitoringTransfer Learning