Many components of existing civil infrastructure systems, such as road pavement, bridges, and buildings, are suffered from rapid aging, which require enormous nation's 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' 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' surface and depth. Some guidelines of applying computer vision techniques are also suggested for each crack inspection application.

In dimensional metrology, contact and non-contact measurement methods each have their own respective strengths and weaknesses. Touch-trigger probes have low uncertainty, and perform well inside deep holes, but have a relatively slow data acquisition speed. By contrast, non-contact digitizers collect high density surface point clouds in seconds, and are much less likely to suffer from sensor collision with the part, but have a higher uncertainty than touch probes. In sheet metal forming, iterative design of the stamping die is needed due to the springback of the sheet metal part. Holes or other features of first article parts may be significantly out of tolerance, so the tactile measurement path created from the Computer Aided Design (CAD) nominal has to be adjusted to avoid cosine error. In more serious cases, probe collisions or missed touches may occur. When measuring holes in thin sheet metal, determination of the touch probe path height is also a challenge if the actual surface location differs from the nominal. To solve this problem and seize the complimentary advantages of contact and non-contact measurement methods, a multi-sensor blue Light Emitting Diode (LED) snapshot sensor and touch-trigger probe inspection system was developed, and affixed to a Coordinate Measuring Machine (CMM). The tactile measurement path was adjusted according to the approximate positions and sizes of the features obtained from the scanner data. The system includes an in-house designed calibration target for scanner calibration and a lightweight 2-axis rotary table for multiple-orientation scanning as well. Software in programming language C for interacting with the scanner and the CMM was developed. A sample stamped sheet metal automobile part was experimentally measured. This system is currently applied to an orthogonal CMM. Suggested future works include implementation on non-Cartesian CMMs, such as articulated arm CMMs, or Computer Numerical Control (CNC) machine tools. / Thesis / Master of Applied Science (MASc)

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