Genetic algorithm optimization of a convolutional neural network for autonomous crack detection

Detecting cracks is an important function in building, tunnel and bridge structural analysis. Successful automation of crack detection can provide a uniform and timely means for preventing further damage to structures. This laboratory has successfully applied convolutional neural networks (CNNs) to online crack detection. CNNs represent an interesting method for adaptive image processing and form a link between artificial neural networks, and finite impulse response filters. As with most artificial neural networks, the CNN is susceptible to multiple local minima, thus complexity and time must be applied in order to avoid becoming trapped within the local minima. This paper employs a standard genetic algorithm (GA) to train the weights of a 4-5x5 filter CNN in order to pass through the local minima. This technique resulted in a 92.3/spl plusmn/1.4% average success rate using 25 GA-trained CNNs presented with 100 crack (320x240 pixel) images.

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