Recognition of mortar pumpability via computer vision and deep learning

The mortar pumpability is essential in the construction industry, which requires much labor to estimate manually and always causes material waste. This paper proposes an effective method by combining a 3-dimensional convolutional neural network (3D CNN) with a 2-dimensional convolutional long short-term memory network (ConvLSTM2D) to automatically classify the mortar pumpability. Experiment results show that the proposed model has an accuracy rate of 100% with a fast convergence speed, based on the dataset organized by collecting the corresponding mortar image sequences. This work demonstrates the feasibility of using computer vision and deep learning for mortar pumpability classification.