Identifying Cracks in Concrete Using a Convolutional Neural Network

SUMMARY - In this project, I build a convolutional neural network (CNN) to classify images of concrete as 'cracked' or 'not cracked'. This exciting use case for machine learning can dramatically improve infrastructure inspections and could even be used to spot defects in an assembly line. The project utilizes Python, Tensorflow and Keras to design the network and the final model achieves over 99% accuracy. Although this is a relatively easy problem to solve, it shows just how powerful machine learning can be. In addition to designing a CNN, the project also contains some useful demonstrations of how to deal with image data in python.

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