Abstract:
This image-processing-based railway track crack detection system is novel. Railway track crack detection requires many image preprocessing steps. Noise-prone image. System filters noise from grayscale image.
Noise reduction improves crack detection. Increase image brightness and convert to binary. This helps system detect only crack and remove unwanted objects. After converting to binary, holes are filled using image processing to reject smaller objects not needed for crack detection. Accuracy relies on intensity.
Blob analysis detects large blobs. Number of connected components detects crack. Number of blobs determines crack detection. Bounding box functionality displays a rectangle around the blob. It inspects railway tracks. The proposed system detects deeper cracks with 80% accuracy and minor cracks with 50-60%.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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