Coating Thickness Measurements and Defect Characterization in Non-Metallic Composite Materials by Using Thermography

Thermography is a non-destructive testing method (NDT), which is widely used to guarantee the quality of non-metallic materials, such as carbon fiber composite, anti-reflection (AR) film, and coatings. As other NDT methods do, thermography determines a defective area based on the signal difference between suspected defective areas and defective-free areas. Two unavoidable effects are decreasing the credibility of thermography detection: one is uneven heating, and the other is lateral diffusion of heat. To solve this problem, researchers have developed various reconstruction methods. Restoring methods are known to have the capacity to reduce the effect of heat-flux lateral diffusion by de-convoluting a point spread function either along a temporal profile or a spatial profile to process captured thermal images. These methods either require pre-knowledge with depth or are not effective in detecting deep defects. Here we propose a spatial-temporal profile-based reconstruction method to reduce the effect of uneven heating and lateral diffusion. The method evaluates the heat flux deposited onto tested samples based on surface temperature gathered under ideal conditions. Then the proposed method is tested in three real applications – in defect detection on semi-transparent materials, on semi-infinite defects (coatings) and anisotropic materials. The method is evaluated against existing methods. Results suggest that the proposed method is effective and computationally efficiently over all the reconstruction methods reviewed. It reduces the effect of uneven heating by providing a good approximation to the input heat flux at the ending image of the sequence

Computer vision-based structural assessment exploiting large volumes of images

Visual assessment is a process to understand the state of a structure based on evaluations originating from visual information. Recent advances in computer vision to explore new sensors, sensing platforms and high-performance computing have shed light on the potential for vision-based visual assessment in civil engineering structures. The use of low-cost, high-resolution visual sensors in conjunction with mobile and aerial platforms can overcome spatial and temporal limitations typically associated with other forms of sensing in civil structures. Also, GPU-accelerated and parallel computing offer unprecedented speed and performance, accelerating processing the collected visual data. However, despite the enormous endeavor in past research to implement such technologies, there are still many practical challenges to overcome to successfully apply these techniques in real world situations. A major challenge lies in dealing with a large volume of unordered and complex visual data, collected under uncontrolled circumstance (e.g. lighting, cluttered region, and variations in environmental conditions), while just a tiny fraction of them are useful for conducting actual assessment. Such difficulty induces an undesirable high rate of false-positive and false-negative errors, reducing the trustworthiness and efficiency of their implementation. To overcome the inherent challenges in using such images for visual assessment, high-level computer vision algorithms must be integrated with relevant prior knowledge and guidance, thus aiming to have similar performance with those of humans conducting visual assessment. Moreover, the techniques must be developed and validated in the realistic context of a large volume of real-world images, which is likely contain numerous practical challenges. In this dissertation, the novel use of computer vision algorithms is explored to address two promising applications of vision-based visual assessment in civil engineering: visual inspection, and visual data analysis for post-disaster evaluation. For both applications, powerful techniques are developed here to enable reliable and efficient visual assessment for civil structures and demonstrate them using a large volume of real-world images collected from actual structures. State-of-art computer vision techniques, such as structure-from-motion and convolutional neural network techniques, facilitate these tasks. The core techniques derived from this study are scalable and expandable to many other applications in vision-based visual assessment, and will serve to close the existing gaps between past research efforts and real-world implementations

Appearance-based material classification after occlusion removal for operation-level construction progress monitoring

Today, the availability of a large number of smart devices on construction sites, has significantly interest popularity of appearance-based methods for automated construction progress using site photographs monitoring. These methods, however, face a number of technical challenges that limit their applicability including low spatial resolution of images, and static and dynamic occlusions due to the construction progress and moving resources (equipment, workers, scaffolding, etc). To address these limitations, this paper extends on an existing model-driven appearance-based material classification method for appearance-based construction progress monitoring using 4D BIM and site photologs. Specifically, it introduces a robust occlusion removal algorithm that can lower false positives in material recognition. The method leverages the depth information from the 4D BIM as well as the 3D point cloud created through Structure from Motion procedures. Once the occluded regions are removed, square-shape patches can be extracted from the back-projection of the BIM elements on site images. These improved image patches are then used in the material recognition pipeline to create a vector quantized histogram of all the material classes. The material class with the highest frequency is chosen as the material type for the element and this appearance information is used to infer the most updated state of progress for the elements. To validate, four existing incomplete and noisy point cloud models from real world construction site images and their corresponding BIMS were used. An extended version of the Construction Material Library (CML) developed at the University of Illinois at Urbana-Champaign’s Real-time and Automated Monitoring and Control (RAAMAC) lab was used to train the material classifiers and the experimental results shows an average accuracy of 90.9%. The occlusion removal and subsequent classification for the four datasets resulted in an accuracy of 92.2% compared to 89.9% in the existing method, demonstrating a definite improvement. By predicting the material present in an element, the status of that element can be identified as “in progress” or “completed’ and compared with the schedule. Since static occlusions are detected, analyzed, and removed, this method has potential to be effective for appearance-based progress monitoring methods and can results in higher accuracy material classification

Applications of Computer Vision Technologies of Automated Crack Detection and Quantification for the Inspection of Civil Infrastructure Systems

Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation\u27s resources from federal and state agencies to inspect and maintain them. Crack is one of important material and structural defects, which must be inspected not only for good maintenance of civil infrastructure with a high quality of safety and serviceability, but also for the opportunity to provide early warning against failure. Conventional human visual inspection is still considered as the primary inspection method. However, it is well established that human visual inspection is subjective and often inaccurate. In order to improve current manual visual inspection for crack detection and evaluation of civil infrastructure, this study explores the application of computer vision techniques as a non-destructive evaluation and testing (NDE&T) method for automated crack detection and quantification for different civil infrastructures. In this study, computer vision-based algorithms were developed and evaluated to deal with different situations of field inspection that inspectors could face with in crack detection and quantification. The depth, the distance between camera and object, is a necessary extrinsic parameter that has to be measured to quantify crack size since other parameters, such as focal length, resolution, and camera sensor size are intrinsic, which are usually known by camera manufacturers. Thus, computer vision techniques were evaluated with different crack inspection applications with constant and variable depths. For the fixed-depth applications, computer vision techniques were applied to two field studies, including 1) automated crack detection and quantification for road pavement using the Laser Road Imaging System (LRIS), and 2) automated crack detection on bridge cables surfaces, using a cable inspection robot. For the various-depth applications, two field studies were conducted, including 3) automated crack recognition and width measurement of concrete bridges\u27 cracks using a high-magnification telescopic lens, and 4) automated crack quantification and depth estimation using wearable glasses with stereovision cameras. From the realistic field applications of computer vision techniques, a novel self-adaptive image-processing algorithm was developed using a series of morphological transformations to connect fragmented crack pixels in digital images. The crack-defragmentation algorithm was evaluated with road pavement images. The results showed that the accuracy of automated crack detection, associated with artificial neural network classifier, was significantly improved by reducing both false positive and false negative. Using up to six crack features, including area, length, orientation, texture, intensity, and wheel-path location, crack detection accuracy was evaluated to find the optimal sets of crack features. Lab and field test results of different inspection applications show that proposed compute vision-based crack detection and quantification algorithms can detect and quantify cracks from different structures\u27 surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application

Target-free vision-based technique for vibration measurements of structures subjected to out-of-plane movements

Vibration measurements have been widely used for structural health monitoring (SHM). Usually, wired sensors are required to attach on the testing structure, which may be arduous, costly and sometimes impossible to install those sensors on the remote and inaccessible part of the structure to be monitored. To overcome the limitations of contact sensors based vibration measurement methods, computer vision and digital image processing based methods have been proposed recently to measure the dynamic displacement of structures. Real-life structure subjected to bi-directional dynamic forces is susceptible to significant out-of-plane movement. Measuring the vibrations of structures under the out-of-plane movements using target-free vision-based methods have not been well studied. This paper proposes a target-free vision-based approach to obtain the vibration displacement and acceleration of structures subjected to out-of-plane movements from minor level excitations. The proposed approach consists of the selection of a region of interest (ROI), key-feature detection and feature extraction, tracking and matching of the features along the entire video, while there is no artificial target attached on the structure. The accuracy of the proposed approach is verified by conducting a number of experimental tests on a reinforced concrete structural column subjected to bi-directional ground motions with peak ground accelerations (PGA) ranging from 0.01 g to 1.0 g. The results obtained by the proposed approach are compared with those measured by using the conventional accelerometer and laser displacement sensor (LDS). It is found that the proposed approach accurately measures the displacement and acceleration time histories of the tested structure. Modal identification is conducted using the measured vibration responses, and natural frequencies can be identified accurately. The results demonstrate that the proposed approach is reliable and accurate to measure the dynamic responses and perform the system modal identification for structural health monitoring

Fast Determination of Soil Behavior in the Capillary Zone Using Simple Laboratory Tests

INE/AUTC 13.1

Coating Thickness Measurements and Defect Characterization in Non-Metallic Composite Materials by Using Thermography

Thermography is a non-destructive testing method (NDT), which is widely used to guarantee the quality of non-metallic materials, such as carbon fiber composite, anti-reflection (AR) film, and coatings. As other NDT methods do, thermography determines a defective area based on the signal difference between suspected defective areas and defective-free areas. Two unavoidable effects are decreasing the credibility of thermography detection: one is uneven heating, and the other is lateral diffusion of heat. To solve this problem, researchers have developed various reconstruction methods. Restoring methods are known to have the capacity to reduce the effect of heat-flux lateral diffusion by de-convoluting a point spread function either along a temporal profile or a spatial profile to process captured thermal images. These methods either require pre-knowledge with depth or are not effective in detecting deep defects. Here we propose a spatial-temporal profile-based reconstruction method to reduce the effect of uneven heating and lateral diffusion. The method evaluates the heat flux deposited onto tested samples based on surface temperature gathered under ideal conditions. Then the proposed method is tested in three real applications – in defect detection on semi-transparent materials, on semi-infinite defects (coatings) and anisotropic materials. The method is evaluated against existing methods. Results suggest that the proposed method is effective and computationally efficiently over all the reconstruction methods reviewed. It reduces the effect of uneven heating by providing a good approximation to the input heat flux at the ending image of the sequence

Aesthetically Relevant Image Captioning

Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesthetically Relevant Image Captioning (ARIC). Based on the observation that most textual comments of an image are about objects and their interactions rather than aspects of aesthetics, we first introduce the concept of Aesthetic Relevance Score (ARS) of a sentence and have developed a model to automatically label a sentence with its ARS. We then use the ARS to design the ARIC model which includes an ARS weighted IAC loss function and an ARS based diverse aesthetic caption selector (DACS). We present extensive experimental results to show the soundness of the ARS concept and the effectiveness of the ARIC model by demonstrating that texts with higher ARS's can predict the aesthetic ratings more accurately and that the new ARIC model can generate more accurate, aesthetically more relevant and more diverse image captions. Furthermore, a large new research database containing 510K images with over 5 million comments and 350K aesthetic scores, and code for implementing ARIC are available at https://github.com/PengZai/ARIC.Comment: Aceepted by AAAI2023. Code and results available at https://github.com/PengZai/ARI

NASA Tech Briefs, February 1996

Topics covered include: Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports