Railway expansion joint gaps and hooks detection using morphological processing, corner points and blobs
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Rail inspection is an essential task in railway maintenance. It is periodically needed for preventing dangerous situations and ensuring safety in railways. In Bangladesh it has been seen many train accidents occur due to over gapping between rail lines and also due to missing of hooks which attach the tracks to the ground. At present, this task is operated manually by a trained human operator who periodically walks along the track searching for visual anomalies. This manual inspection is lengthy, laborious and subjective. This thesis presents a machine vision-based technique to automatically detect the presence of rail line hook and measure the gaps between each line to check whether the gap is safe or not. This inspection system uses real images acquired by a digital line scan camera installed under an automatic vehicle. Data are processed according to a combination of image processing and pattern recognition methods to achieve high performance automated detection. The scope of this project is strictly limited to the development of a machine vision based program capable of detecting the presence of parts of interest in rail tracks, from given rail track images.