Autonomous concrete crack detection using deep fully convolutional neural network

Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring

This article implements the state‐of‐the‐art deep learning technologies for a civil engineering application, namely recognition of structural damage from images. Inspired by ImageNet Chall...

Deep Transfer Learning for Image-Based Structural Damage Recognition

The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures However, the current manual crack description 

Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network

Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete

Computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real-time simultaneous detection of multiple types of damages, a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R-CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures. Its performance is also compared to that of the traditional CNN-based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real-time damage detection on video using the trained networks is developed.

Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures

The primary method of structural health monitoring is human-based visual inspection, which—despite its limitations of consistency and accessibility—can warn about changes in a bridge’s condition. To improve the visual inspection of civil infrastructure and address these drawbacks of human-oriented inspection, computer vision-based techniques have been developed to detect structural damage in images. Most of these methods, however, detect only specific types of damage, such as cracks in concrete or steel. Another drawback is that the traditional convolutional neural network-based damage detection method is not able to provide the location of the detected damage. To provide quasi-realtime simultaneous detection and localization of multiple types of damage, a structural damage detection method based on Faster Regionbased Convolutional Neural Network (Faster R-CNN) is proposed. The original architecture of Faster R-CNN is modified, trained, validated, and tested for this study. The robustness of the trained Faster R-CNN is evaluated and demonstrated using seven new images taken of various structures.

Deep faster R-CNN-based automated detection and localization of multiple types of damage

Cold climate regions with sustained temperatures between −10°C and −20°C offer a unique opportunity to produce temporary rigid ice spatial structures. An advantage offered by creating these unusual...

Gahyun Suh

Papers

Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

Real-time multiple damage mapping using autonomous UAV and deep faster region-based neural networks for GPS-denied structures

Deep faster R-CNN-based automated detection and localization of multiple types of damage

A study of digital and physical workflows used for the creation of fabric-formed ice shells with bending active frames: