StressNet: A deep convolutional neural network for recovering the stress field from isochromatic images

Extending photoelasticity studies to industrial applications is a complex process generally limited by the image acquisition assembly and the computational methods for demodulating the stress field wrapped into the color fringe patterns. In response to such drawbacks, this paper proposes an auto-encoder based on deep convolutional neural networks, called StressNet, to recover the stress map from one single isochromatic image. In this case, the public dataset of synthetic photoelasticity images `Isochromatic-art' was used for training and testing achieving an averaged performance of 0.95 +/- 0.04 according to the structural similarity index. With these results, the proposed network is capable of obtaining a continuous stress surface which represents a great opportunity toward developing real time stress evaluations.