So it has been discussed for decades on how best to photograph and catalogue, report, repair pavements which are an expensive overhead in these austere times. The team Zhun Fan, Yuming Wu and Jiewei Lu have just published their paper, (Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network Feb 2018) describing their amazing technology and findings.
In their study they used images taken from an iphone5 of pavements in Beijing and another set of images from from French pavements. They ran a number of training and testing routines, both using current methods and their proposed method. The conclusion being that the proposed method shows a better performance of dealing with pavement texture, and predicts the crack structure much closer to manual methods that current automated methods. CNN has a good ability to learn about images, and the network architecture can be used for other image methods, not only photographic. Being close to the manual method means that soon continuous rapid photographs of pavements could be taken and cracks identified and labelled without human interaction.
Next time you see a broken paving slab, think that it may soon become a thing of the past as a drive by camera will be identifying and reporting the flaw sooner rather than later.
Telanova : Managed IT http://tnova.uk/pavements