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 | , , , , , , |
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Format | Patent |
Language | Chinese 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 |
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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 |
Author_xml | – fullname: LIU JIAN – fullname: DENG XI – fullname: CAO XINYU – fullname: LIU SHOUSONG – fullname: WANG KAI – fullname: YANG YINGMING – fullname: QIAN QI |
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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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