Infrared Thermography for Weld Inspection: Feasibility and Application

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS

SDNET2021: Annotated NDE Dataset for Structural Defects

SDNET2021 is a uniquely validated annotated dataset for evaluating the condition of concrete bridge decks and benchmarking advanced deep learning models for defects (delamination, cracks, rebar corrosion) detection and bridge deck evaluation. Common structural defects, such as cracks, delamination, spalling, rebar corrosion, etc. are commonly detected using traditional hands-on inspections (visual, destructive, chain dragging, and sounding). These methods are accompanied with limitations such as disruption and closure of traffic, laborious, costly, time consuming and possible inconsistencies and likelihood of errors in field data collection and interpretation. Usually there exists dataset for surface defects from laboratory specimens, but rare validated datasets for sub-surface defects of several NDE techniques exists. SDNET2021 contains 1,936 annotated IE signals, over 663,102 annotated GPR signals and five (5) mosaic annotated IRT images containing about 4,580,680 annotated pixels collected during 2020 summer from five (5) in-service bridge decks in Grand forks, ND, USA. These datasets were annotated with a set of ground truth maps representing the class of delamination at each point of the decks after defected concrete was removed based on chain-dragging, i.e. ground truth data. They were also validated with conventional image processing, Fast Fourier Transform (FFT) maximum frequency, B-scan techniques, and locations of exposed corroded rebar. The ground truth maps also show the GPS coordinates and size of each class of removal for the delaminated portions of the bridge decks under investigation. This ground truth was developed on site prior to commencement of repair to show sound concrete Class 1 (No Delamination); Class 2 Delamination (delamination above top bar mat), and Class 3 Delamination (delamination below top bar mat). The IRT, GPR and IE data has been annotated and validated with the ground truth data collected during the investigation. SDNET2021 will be highly significant in further studies related to the development of algorithms based on AI models for classification and delamination/defects detection, which is a major frontline subject for continued research in the field of advanced NDE and structural health monitoring. SDNET2021 is freely available at https://doi.org/10.31356/data019 This version has been revised and updated with further data validation and stands to be the current and most up-to-date dataset

Bridge Inspection: Human Performance, Unmanned Aerial Systems and Automation

Unmanned aerial systems (UASs) have become of considerable private and commercial interest for a variety of jobs and entertainment in the past 10 years. This paper is a literature review of the state of practice for the United States bridge inspection programs and outlines how automated and unmanned bridge inspections can be made suitable for present and future needs. At its best, current technology limits UAS use to an assistive tool for the inspector to perform a bridge inspection faster, safer, and without traffic closure. The major challenges for UASs are satisfying restrictive Federal Aviation Administration regulations, control issues in a GPS-denied environment, pilot expenses and availability, time and cost allocated to tuning, maintenance, post-processing time, and acceptance of the collected data by bridge owners. Using UASs with self-navigation abilities and improving image-processing algorithms to provide results near real-time could revolutionize the bridge inspection industry by providing accurate, multi-use, autonomous three-dimensional models and damage identification

Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations

Concrete cracking is a ubiquitous phenomenon, present in all types of concrete structures. Identifying and tracking the amount and severity of cracking is paramount to evaluating the current condition and predicting the future service life of a concrete asset. Concrete cracks can indicate reinforcement corrosion, the development of spalls or changing support conditions. Therefore, monitoring cracks during the life span of concrete structures has been an effective technique to evaluate the level of safety and preparing plans for future appropriate rehabilitation. One growing technique are unmanned inspections using Unmanned Aerial Vehicles (UAV). UAVs are drones equipped with cameras, sensors, GPS, etc. RGB images (color images in Red, Green and Blue color space) are obtained from a camera mounted on a UAV flying around the structure, to detect cracks and other defects. Each image captured by UAV needs to be evaluated to track the crack formations. To save time, this task can be done by applying image processing techniques to automatically detect and report cracks rather than using a human to identify them. In addition, processing RGB images with sufficient information, such as the distance of camera to surface for each picture, will provide the dimension of the cracks (length and width). The report consists of the following sections: A literature review of image processing techniques used in structural health monitoring and other fields of interest is provided in chapter 2. The Proposed method to identify cracks is demonstrated in Chapter 3. Experimental results, conclusion and future work are presented in Chapter 4. Appendix A includes the processed images using the proposed method and Appendix B includes the comparison between Talab’s method and the proposed method. In Appendix C, a “readme” file is given to run the program, and finally Appendix D shows the Matlab Code

Infrared Thermography for Weld Inspection: Feasibility and Application

Traditional ultrasonic testing (UT) techniques have been widely used to detect surface and sub-surface defects of welds. UT inspection is a contact method which burdens the manufacturer by storing hot specimens for inspection when the material is cool. Additionally, UT is only valid for 5 mm specimens or thicker and requires a highly skilled operator to perform the inspections and interpret the signals. Infrared thermography (IRT) has the potential to be implemented for weld inspections due to its non-contact nature. In this study, the feasibility of using IRT to overcome the limitations of UT inspection is investigated to detect inclusion, porosity, cracking, and lack of fusion in 38 weld specimens with thicknesses of 3, 8 and 13 mm. UT inspection was also performed to locate regions containing defects in the 8 mm and 13 mm specimens. Results showed that regions diagnosed with defects by the UT inspection lost heat faster than the sound weld. The IRT method was applied to six 3 mm specimens to detect their defects and successfully detected lack of fusion in one of them. All specimens were cut at the locations indicated by UT and IRT methods which proved the presence of a defect in 86% of the specimens. Despite the agreement with the UT inspection, the proposed IRT method had limited success in locating the defects in the 8 mm specimens. To fully implement in-line IRT-based weld inspections more investigations are required

SDNET2018: An Annotated Image Dataset for Non-Contact Concrete Crack Detection Using Deep Convolutional Neural Networks

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep convolutional neural networks (DCNNs), which are a subject of continued research in the field of structural health monitoring. The authors present benchmark results for crack detection using SDNET2018 and a crack detection algorithm based on the AlexNet DCNN architecture. SDNET2018 is freely available at https://doi.org/10.15142/T3TD19

Comparison of Deep Convolutional Neural Networks and Edge Detectors for Image-Based Crack Detection in Concrete

This paper compares the performance of common edge detectors and deep convolutional neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of 19 high definition images (3420 sub-images, 319 with cracks and 3101 without) of concrete is analyzed using six common edge detection schemes (Roberts, Prewitt, Sobel, Laplacian of Gaussian, Butterworth, and Gaussian) and using the AlexNet DCNN architecture in fully trained, transfer learning, and classifier modes. The relative performance of each crack detection method is compared here for the first time on a single dataset. Edge detection methods accurately detected 53–79% of cracked pixels, but they produced residual noise in the final binary images. The best of these methods was useful in detecting cracks wider than 0.1 mm. DCNNs were used to label images, and accurately labeled them with 99% accuracy. In transfer learning mode, the network accurately detected about 86% of cracked images. DCNNs also detected much finer cracks than edge detection methods. In fully trained and classifier modes, the network detected cracks wider than 0.08 mm; in transfer learning mode, the network was able to detect cracks wider than 0.04 mm. Computational times for DCNN are shorter than the most efficient edge detection algorithms, not considering the training process. These results show significant promise for future adoption of DCNN methods for image-based damage detection in concrete. To reduce the residual noise, a hybrid method was proposed by combining the DCNN and edge detectors which reduced the noise by a factor of 24

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare dierent edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS