Recognition of mortar pumpability via computer vision and deep learning  

作　　者：Hao-Zhe Feng Hong-Yang Yu Wen-Yong Wang Wen-Xuan Wang Ming-Qian Du 

机构地区：[1]Research Institute Electronic Science and Technology,University of Electronic Science and Technology of China,Chengdu,611731,China [2]School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu,611731,China [3]The Second Research Institute of Civil Aviation Administration of China,Chengdu,610041,China

出　　处：《Journal of Electronic Science and Technology》2023年第3期73-81,共9页电子科技学刊（英文版）

基　　金：supported by the Key Project of National Natural Science Foundation of China-Civil Aviation Joint Fund under Grant No.U2033212。

摘　　要：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.

关 键 词：Classification Computer vision Deep learning PUMPABILITY 2-dimensional convolutional long short-term memory network (ConvLSTM2D) 3-dimensional convolutional neural network(3D CNN) 

分 类 号：J943[艺术—电影电视艺术] TP18[自动化与计算机技术—控制理论与控制工程]