First of all image pre-processing has been done by making use of Opencv(Canny Edge and Auto canny, Clahe, various type of Morphological operation like Erosion , Dilation , Medium blurr for feature extraction in&nbsp;image).
Then annotate the images for better understanding of crack area &amp; locating ground truth in image. Took annotated image reading in xml file the converted it to CSV file &amp; for applying it to tensorflow vector converted it to .rec file.
Next step is to use pre-processed image &amp; converted file and applying it to built in SSD-Inception model for having good accuracy &amp; took inference of images.
Calculated the mAP, Precision, Recall, Classification Accuracy<ul><li>Python,</li><li>Machine Learning (ML),</li><li>Artificial Intelligence</li></ul>

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Wall Surface Crack Detection

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