Material effective area displacement field measurement method based on convolutional LSTM neural network

The invention relates to a material effective area displacement field measurement method based on a convolutional LSTM neural network, and the method comprises the following steps: randomly spraying speckles on the surface of a material, and continuously collecting images through a camera, so as to record the deformation process of the material under the action of an external force; constructing a material deformation image sequence as a data set, wherein the data set comprises a training set and a test set; by combining convolution, deconvolution, multi-task and convolution LSTM neural network, establishing a neural network model for measuring a material displacement field in real time and segmenting a crack area by inputting an image sequence of material deformation; training a multi-task convolutional LSTM neural network model for material displacement field measurement and crack region segmentation by using the training set data. and inputting time sequence image data acquired by the camera, measuring a r