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The detection of defects or cracks in rail track plays an important role in railway management, which prevents train accidents in both summer and rainy seasons. During summer, the cracks are formed on the track which slips the train wheel. In rainy environment, the rail tracks are affected by corrosion which also produced cracks on it. In present method, the cracks or defects are detected Echo image display device or semi-conduction magnetism sensor devices which consumes more time. The proposed method enhances the track image using adaptive histogram equalization technique and further feature are extracted from the enhanced rail track image. In this proposes system we demonstrate that CNN can be used to localize cracks on railway track surfaces and obtain their corresponding masks to aid extract other properties that are useful for inspection. Such a tool can help mitigate the drawbacks of manual

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