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- Title
- VISUAL INSPECTION OF RAILROAD TRACKS.
- Creator
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Babenko, Pavel, Shah, Mubarak, University of Central Florida
- Abstract / Description
-
In this dissertation, we have developed computer vision methods for measurement of rail gauge, and reliable identification and localization of structural defects in railroad tracks. The rail gauge is the distance between the innermost sides of the two parallel steel rails. We have developed two methods for evaluation of rail gauge. These methods were designed for different hardware setups: the first method works with two pairs of unaligned video cameras while the second method works with...
Show moreIn this dissertation, we have developed computer vision methods for measurement of rail gauge, and reliable identification and localization of structural defects in railroad tracks. The rail gauge is the distance between the innermost sides of the two parallel steel rails. We have developed two methods for evaluation of rail gauge. These methods were designed for different hardware setups: the first method works with two pairs of unaligned video cameras while the second method works with depth maps generated by paired laser range scanners. We have also developed a method for detection of rail defects such as damaged or missed rail fasteners, tie clips, and bolts, based on correlation and MACH filters. Lastly, to make our algorithms perform in real-time, we have developed the GPU based library for parallel computation of the above algorithms. Rail gauge is the most important measurement for track maintenance, because deviations in gauge indicate where potential defects may exist. We have developed a vision-based method for rail gauge estimation from a pair of industrial laser range scanners. In this approach, we start with building a 3D panorama of the rail out of a stack of input scans. After the panorama is built, we apply FIR circular filtering and Gaussian smoothing to the panorama buffer to suppress the noise component. In the next step we attempt to segment the rail heads in the panorama buffer. We employ the method which detects railroad crossings or forks in the panorama buffer. If they are not present, we find the rail edge using robust line fit. If they are present we use an alternative way: we predict the rail edge positions using Kalman filter. In the next step, common to both fork/crossings conditions, we find the adjusted positions of rail edges using additional clustering in the vicinity of the edge. We approximate rail head surface by the third degree polynomial and then fit two plane surfaces to find the exact position of the rail edge. Lastly, using rail edge information, we calculate the rail gauge and smooth it with 1D Gaussian filter. We have also developed a vision-based method to estimate the rail gauge from a pair of unaligned high shutter speed calibrated cameras. In this approach, the first step is to accurately detect the rail in each of the two non-overlapping synchronous images from the two cameras installed on the data collection cart by building an edge map, and fitting lines into the edge map using the Hough transform, and detecting persistent edge lines using a history buffer. After railroad track parts are detected, we segment rails out to find rail edges and calculate the rail gauge. We have demonstrated how to apply Computer Vision methods (the correlation filters and MACH filters in particular) to find different types of railroad elements with fixed or similar appearance, like railroad clips, bolts, and rail plates, in real-time. Template-based approaches for object detection (correlation filters) directly compare gray scale image data to a predefined model or template. The drawback of the correlation filters has always been that they are neither scale nor rotation invariant, thus many different filters are needed if either scale or rotation change. The application of many filters cannot be done in real-time. We have succeeded to overcome this difficulty by using the parallel computation technology which is widely available in the GPUs of most advanced graphics cards. We have developed a library, MinGPU, which facilitates the use of GPUs for Computer Vision, and have also developed a MinGPU-based library of several Computer Vision methods, which includes, among others, an implementation of correlation filters on the GPU. We have achieved a true positive rate of 0.98 for fastener detection using implementation of MACH filters on GPU. Besides correlation filters, MinGPU include implementations of Lucas-Kanade Optical Flow, image homographies, edge detectors and discrete filters, image pyramids, morphology operations, and some graphics primitives. We have shown that MinGPU implementation of homographies speeds up execution time approximately 600 times versus C implementation and 8000 times versus Matlab implementation. MinGPU is built upon a reusable core and thus is an easily expandable library. With the help of MinGPU, we have succeeded to make our algorithms work in real-time.
Show less - Date Issued
- 2009
- Identifier
- CFE0002895, ucf:48038
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0002895
- Title
- Simulation, Analysis, and Optimization of Heterogeneous CPU-GPU Systems.
- Creator
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Giles, Christopher, Heinrich, Mark, Ewetz, Rickard, Lin, Mingjie, Pattanaik, Sumanta, Flitsiyan, Elena, University of Central Florida
- Abstract / Description
-
With the computing industry's recent adoption of the Heterogeneous System Architecture (HSA) standard, we have seen a rapid change in heterogeneous CPU-GPU processor designs. State-of-the-art heterogeneous CPU-GPU processors tightly integrate multicore CPUs and multi-compute unit GPUs together on a single die. This brings the MIMD processing capabilities of the CPU and the SIMD processing capabilities of the GPU together into a single cohesive package with new HSA features comprising better...
Show moreWith the computing industry's recent adoption of the Heterogeneous System Architecture (HSA) standard, we have seen a rapid change in heterogeneous CPU-GPU processor designs. State-of-the-art heterogeneous CPU-GPU processors tightly integrate multicore CPUs and multi-compute unit GPUs together on a single die. This brings the MIMD processing capabilities of the CPU and the SIMD processing capabilities of the GPU together into a single cohesive package with new HSA features comprising better programmability, coherency between the CPU and GPU, shared Last Level Cache (LLC), and shared virtual memory address spaces. These advancements can potentially bring marked gains in heterogeneous processor performance and have piqued the interest of researchers who wish to unlock these potential performance gains. Therefore, in this dissertation I explore the heterogeneous CPU-GPU processor and application design space with the goal of answering interesting research questions, such as, (1) what are the architectural design trade-offs in heterogeneous CPU-GPU processors and (2) how do we best maximize heterogeneous CPU-GPU application performance on a given system. To enable my exploration of the heterogeneous CPU-GPU design space, I introduce a novel discrete event-driven simulation library called KnightSim and a novel computer architectural simulator called M2S-CGM. M2S-CGM includes all of the simulation elements necessary to simulate coherent execution between a CPU and GPU with shared LLC and shared virtual memory address spaces. I then utilize M2S-CGM for the conduct of three architectural studies. First, I study the architectural effects of shared LLC and CPU-GPU coherence on the overall performance of non-collaborative GPU-only applications. Second, I profile and analyze a set of collaborative CPU-GPU applications to determine how to best optimize them for maximum collaborative performance. Third, I study the impact of varying four key architectural parameters on collaborative CPU-GPU performance by varying GPU compute unit coalesce size, GPU to memory controller bandwidth, GPU frequency, and system wide switching fabric latency.
Show less - Date Issued
- 2019
- Identifier
- CFE0007807, ucf:52346
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0007807
