A hierarchical semantic segmentation framework for computer vision-based bridge damage detection

by   Jingxiao Liu, et al.

Computer vision-based damage detection using remote cameras and unmanned aerial vehicles (UAVs) enables efficient and low-cost bridge health monitoring that reduces labor costs and the needs for sensor installation and maintenance. By leveraging recent semantic image segmentation approaches, we are able to find regions of critical structural components and recognize damage at the pixel level using images as the only input. However, existing methods perform poorly when detecting small damages (e.g., cracks and exposed rebars) and thin objects with limited image samples, especially when the components of interest are highly imbalanced. To this end, this paper introduces a semantic segmentation framework that imposes the hierarchical semantic relationship between component category and damage types. For example, certain concrete cracks only present on bridge columns and therefore the non-column region will be masked out when detecting such damages. In this way, the damage detection model could focus on learning features from possible damaged regions only and avoid the effects of other irrelevant regions. We also utilize multi-scale augmentation that provides views with different scales that preserves contextual information of each image without losing the ability of handling small and thin objects. Furthermore, the proposed framework employs important sampling that repeatedly samples images containing rare components (e.g., railway sleeper and exposed rebars) to provide more data samples, which addresses the imbalanced data challenge.


page 6

page 7

page 8


Autonomous damage assessment of structural columns using low-cost micro aerial vehicles and multi-view computer vision

Structural columns are the crucial load-carrying components of buildings...

High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage

Visual inspection is predominantly used to evaluate the state of civil s...

Damage Vision Mining Opportunity for Imbalanced Anomaly Detection

In past decade, previous balanced datasets have been used to advance alg...

Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks

Image data has a great potential of helping post-earthquake visual inspe...

dacl10k: Benchmark for Semantic Bridge Damage Segmentation

Reliably identifying reinforced concrete defects (RCDs)plays a crucial r...

Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding

Image data has a great potential of helping conventional visual inspecti...

Per-pixel Classification Rebar Exposures in Bridge Eye-inspection

Efficient inspection and accurate diagnosis are required for civil infra...

Please sign up or login with your details

Forgot password? Click here to reset