Recognition of mortar pumpability via computer vision and deep learning

Q1 Engineering

Journal of Electronic Science and Technology Pub Date : 2023-09-01 DOI:10.1016/j.jnlest.2023.100215

Hao-Zhe Feng , Hong-Yang Yu , Wen-Yong Wang , Wen-Xuan Wang , Ming-Qian Du

引用次数: 0

Abstract

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.

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基于计算机视觉和深度学习的砂浆可泵性识别

砂浆的可泵性在建筑行业中是必不可少的，它需要大量的人工估算，并且经常造成材料的浪费。本文提出了一种将三维卷积神经网络(3D CNN)与二维卷积长短期记忆网络(ConvLSTM2D)相结合的砂浆可泵性自动分类的有效方法。实验结果表明，基于采集相应的迫击炮图像序列组织的数据集，该模型具有100%的准确率和较快的收敛速度。这项工作证明了使用计算机视觉和深度学习进行砂浆可泵性分类的可行性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering

CiteScore

4.30

自引率

0.00%

发文量

1362

审稿时长

99 days

期刊介绍： JEST (International) covers the state-of-the-art achievements in electronic science and technology, including the most highlight areas: ¨ Communication Technology ¨ Computer Science and Information Technology ¨ Information and Network Security ¨ Bioelectronics and Biomedicine ¨ Neural Networks and Intelligent Systems ¨ Electronic Systems and Array Processing ¨ Optoelectronic and Photonic Technologies ¨ Electronic Materials and Devices ¨ Sensing and Measurement ¨ Signal Processing and Image Processing JEST (International) is dedicated to building an open, high-level academic journal supported by researchers, professionals, and academicians. The Journal has been fully indexed by Ei INSPEC and has published, with great honor, the contributions from more than 20 countries and regions in the world.

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