Image processing based object measurement system
Object measurement systems play a critical role in various fields, including manufacturing, engineering, robotics, and scientific research where accurate and precise measurements are essential. Imaging enables non-contact object measurement which is useful for many applications. With the expansion o...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2023
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| Online Access: | http://eprints.utar.edu.my/5888/1/SE_1904105_FYP_report_%2D_TanChunGuan_%2D_CHUN_GUAN_TAN.pdf http://eprints.utar.edu.my/5888/ |
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| Summary: | Object measurement systems play a critical role in various fields, including manufacturing, engineering, robotics, and scientific research where accurate and precise measurements are essential. Imaging enables non-contact object measurement which is useful for many applications. With the expansion of automation and quality control, object measurement using image processing has increased. However, it is still challenging for the imaging-based object measurement to accurately handle objects with complex structures, objects of different shapes and sizes, manage the impact of in environmental conditions, and efficiently handle highvolume measurement tasks. To investigate these problems, this research project evaluated three existing object measurement systems and compared their performance in terms of accuracy, flexibility, environmental robustness, and efficiency. These systems include systems without reference objects, systems with reference objects, and systems that utilize ArUco markers. Experiments were conducted using objects of different shapes and sizes under different environmental conditions to identify the strengths and limitations of each system. The results show that each system has its own limitations. Based on these findings, an object measurement system has been proposed and developed with the aim to enhance the strengths of the existing systems and overcomes their limitations. The proposed system utilizes image processing, feature extraction, and geometric modelling techniques to accurately estimate object dimensions and position with an average error rate of less than 2%. The outcome of this project is hoped to benefit various industrial and scientific processes that rely on precise measurement data such as manufacturing, quality control, engineering, etc. |
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