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

TL;DR

This paper introduces an unsupervised anomaly detection method using generative adversarial networks to identify concrete damage from drone images by reversing damaged images to healthy states.

Contribution

It proposes a novel unpaired image-to-image translation approach for concrete damage detection, reducing the need for extensive annotated datasets.

Findings

Effective damage detection in field drone images

Unsupervised method reduces annotation effort

Demonstrated usefulness in concrete health monitoring

Abstract

In order to monitor the state of large-scale infrastructures, image acquisition by autonomous flight drones is efficient for stable angle and high-quality images. Supervised learning requires a large data set consisting of 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 image is a generator input, it tends to reverse from the damaged state to the healthy state generated image. Using the distance of distribution between the real damaged image and the generated reverse aging healthy state fake image, it is possible to detect the concrete damage automatically from unsupervised learning. This paper proposes an anomaly detection method using unpaired image-to-image translation mapping from damaged images to reverse aging fakes that approximates healthy conditions. We apply our method to field studies, and we examine the usefulness of our method for health monitoring of concrete damage.