a ML based pavement crack detection
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Positive samples: patch with black pixels (crack) at center
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Negative samples: patch with white pixels (non-crack) at center
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CNN: multi-label classification
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Input : SxS patch around a sample (shape: SxSx3)
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Output: structure around the sample in a KxK patch (shape: K^2 )
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HOG: binary classification
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Input : HOG features from SxS patch around a sample
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Output: class label of the sample (crack: c=0, non-crack: c=1)
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data collection
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Use each pixel to generate an input patch
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Predict output value for each patch
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For HOG:
- if output > 0.5 ==> pixel is crack
- if output < 0.5 ==> pixel is non-crack
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For CNN:
- multiple output values (overlapping)
- at each pixel ouput value is affected from KxK surrounding pixels
- summation and normalization ==> probability map
- if pmap > 0.5 ==> pixel is crack
- if pmap < 0.5 ==> pixel is non-crack
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Results:
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CNN (S=5)
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HOG (S=1)
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HOG (S=5)
