Cracking is a major pavement distress that jeopardizes road serviceability and traffic safety. Automated pavement distress survey (APDS) systems have been developed using digital imaging technology to replace human surveys for more timely and accurate inspections. Most APDS systems require special lighting devices to illuminate pavements and prevent shadows of roadside objects that distort cracks in the image. Most of the artificial lighting devices are laser based, which are either hazardous to unprotected people, or require dedicated power supplies on the vehicle. This study is aimed to develop a new imaging system that can scan pavement surface at highway speed and determine the severity level of pavement cracking without using any artificial lighting. The new system consists of dual line-scan cameras that are installed side by side to scan the same pavement area as the vehicle moves. Cameras are controlled with different exposure settings so that both sunlit and shadowed areas can be visible in two separate images. The paired images contain complementary details useful for reconstructing an image in which the shadows are eliminated. This paper intends to presents (1) the design of the dual line-scan camera system for a high-speed pavement imaging system that does not require artificial lighting, (2) a new calibration method for line-scan cameras to rectify and register paired images, which does not need mechanical assistance for dynamical scan, (3) a customized image-fusion algorithm that merges the multi-exposure images into one shadow-free image for crack detection, and (4) the results of the field tests on a selected road over a long period. / text

This thesis deals with the design and subsequent implementation of a unit inspecting a printed logos on the pen surface. A line-scan camera is used to capture the object. Whole the unit including acquited data processing is controlled by Raspberry Pi 4 platform extended by perifery board. The control of the hardware parts is implemented in C++, the detection algorithms in Python using OpenCV and TensorFlow libraries. The unit has a graphical user interface for control of the inspection process. In the end of the thesis test of the unit reliability is shown.

The great social and economic impact of earthquakes has made necessary the development of novel structural health monitoring (SHM) solutions for increasing the level of structural safety and assessment. SHM is the process of comparing the current state of a structure’s condition relative to a healthy baseline state to detect the existence, location, and degree of likely damage during or after a damaging input, such as an earthquake. Many SHM algorithms have been proposed in the literature. However, a large majority of these algorithms cannot be implemented in real time. Therefore, their results would not be available during or immediately after a major event for urgent post-event response and decision making. Further, these off-line techniques are not capable of providing the input information required for structural control systems for damage mitigation. The small number of real-time SHM (RT-SHM) methods proposed in the past, resolve these issues. However, these approaches have significant computational complexity and typically do not manage nonlinear cases directly associated with relevant damage metrics. Finally, many available SHM methods require full structural response measurement, including velocities and displacements, which are typically difficult to measure. All these issues make implementation of many existing SHM algorithms very difficult if not impossible. This thesis proposes simpler, more suitable algorithms utilising a nonlinear Bouc-Wen hysteretic baseline model for RT-SHM of a large class of nonlinear hysteretic structures. The RT-SHM algorithms are devised so that they can accommodate different levels of the availability of design data or measured structural responses, and therefore, are applicable to both existing and new structures. The second focus of the thesis is on developing a high-speed, high-resolution, seismic structural displacement measurement sensor to enable these methods and many other SHM approaches by using line-scan cameras as a low-cost and powerful means of measuring structural displacements at high sampling rates and high resolution. Overall, the results presented are thus significant steps towards developing smart, damage-free structures and providing more reliable information for post-event decision making.

The Doctoral thesis demonstrates the design of reliable industrial visual systems. The special emphasis is dedicated to the detection of defects on webs in industrial applications based on line-scan cameras. This system makes possible detection and classification of defects originating during the real production conditions. This work covers a theoretical study of a visual system for the defect detection on endless bands as well as of appropriate lighting and the scene arrangement. Further to that have been selected, adjusted and designed key components of hardware. Following the design and optimization of algorithms a system prototype had been installed on non-woven textiles production line. Eight visual systems implemented into real-life industrial conditions based on this prototype