English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In order to monitor the state of large-scale infrastructures, imageacquisition by autonomous flight drones is efficient for stable angle andhigh-quality images. Supervised learning requires a large data set consistingof images and annotation labels. It takes a long time to accumulate images,including identifying the damaged regions of interest (ROIs). In recent years,unsupervised deep learning approaches such as generative adversarial networks(GANs) for anomaly detection algorithms have progressed. When a damaged imageis a generator input, it tends to reverse from the damaged state to the healthystate generated image. Using the distance of distribution between the realdamaged image and the generated reverse aging healthy state fake image, it ispossible to detect the concrete damage automatically from unsupervisedlearning. This paper proposes an anomaly detection method using unpairedimage-to-image translation mapping from damaged images to reverse aging fakesthat approximates healthy conditions. We apply our method to field studies, andwe examine the usefulness of our method for health monitoring of concretedamage.

Generative Damage Learning for Concrete Aging Detection using Auto-flight Images