Construction material visual inventory method based on lightweight deep neural network

The invention relates to the technical field of computer vision, in particular to a construction material visual inventory method based on a lightweight deep neural network, and the method comprises the steps: collecting a building material data set, marking a steel bar data set through manual combi...

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Main Authors LIU JIAN, DENG XI, CAO XINYU, LIU SHOUSONG, WANG KAI, YANG YINGMING, QIAN QI
Format Patent
LanguageChinese
English
Published 08.12.2023
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Abstract The invention relates to the technical field of computer vision, in particular to a construction material visual inventory method based on a lightweight deep neural network, and the method comprises the steps: collecting a building material data set, marking a steel bar data set through manual combination with semi-automatic marking, and dividing the steel bar data set into a training set and a test set; a lightweight neural network model is designed on the basis of Improved ShufflNet v2; training the lightweight neural network model by using the constructed training set, and preliminarily testing the performance of the lightweight neural network model by using the test set; performing channel pruning on the trained lightweight neural network model based on a BN layer, simplifying the lightweight neural network model, and testing the performance of the pruned lightweight neural network model by using the test set again; and detecting the reinforcing steel bar image by using the trained and pruned lightweight
AbstractList The invention relates to the technical field of computer vision, in particular to a construction material visual inventory method based on a lightweight deep neural network, and the method comprises the steps: collecting a building material data set, marking a steel bar data set through manual combination with semi-automatic marking, and dividing the steel bar data set into a training set and a test set; a lightweight neural network model is designed on the basis of Improved ShufflNet v2; training the lightweight neural network model by using the constructed training set, and preliminarily testing the performance of the lightweight neural network model by using the test set; performing channel pruning on the trained lightweight neural network model based on a BN layer, simplifying the lightweight neural network model, and testing the performance of the pruned lightweight neural network model by using the test set again; and detecting the reinforcing steel bar image by using the trained and pruned lightweight
Author QIAN QI
LIU SHOUSONG
YANG YINGMING
DENG XI
LIU JIAN
WANG KAI
CAO XINYU
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DocumentTitleAlternate 一种基于轻量化深度神经网络的建造物料视觉盘存方法
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RelatedCompanies CHINA BUILDING TECHNIQUE GROUP CO., LTD
CHONGQING UNIVERSITY
CHINA ACADEMY OF BUILDING RESEARCH
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Snippet The invention relates to the technical field of computer vision, in particular to a construction material visual inventory method based on a lightweight deep...
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Title Construction material visual inventory method based on lightweight deep neural network
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