土木与环境工程学报（中英文）2024，Vol.46Issue(1)：93-101,9.DOI:10.11835/j.issn.2096-6717.2022.026
基于卷积神经网络的预制叠合板多目标智能化检测方法
Multi-target intelligent detection method of prefabricated laminated board based on convolutional neural network
摘要
Abstract
The unqualified size of prefabricated component in the production process will lead to the failure of the installation on the construction site,and affect the construction period.In order to promote the process of intelligent production of prefabricated components.Based on a convolutional neural network,the prefabricated laminated board is used as an example to study the intelligent detection method of the production process.Design and install an image acquisition system on the production line,establish a prefabricated laminated board detection data set,and use the YOLOv5 algorithm to detect the concrete plate,the embedded PVC junction box and the overhanging steel bar.The fixed magnetic box is used as the benchmark to analyze the detection error of the dimension of the concrete plate and the coordinate of the embedded PVC junction box,and maintains a high recognition accuracy with a smaller parameter scale of the training data set.The result shows that the method can effectively detect the number and dimension of the concrete plate,the number and coordinate of the embedded PVC junction box,and detect the overhanging steel bar of unqualified bending direction.The method can reduce labor costs,improve detection accuracy,speed up detection process,and improve the delivery quality of prefabricated laminated board.关键词
预制叠合板/多目标检测/卷积神经网络/预制构件/智能化生产Key words
prefabricated laminated board/multi-target detection/convolutional neural network/prefabricated component/intelligent production分类
土木建筑引用本文复制引用
姚刚,廖港,杨阳,李青泽,魏伏佳..基于卷积神经网络的预制叠合板多目标智能化检测方法[J].土木与环境工程学报（中英文）,2024,46(1):93-101,9.基金项目
国家重点研发计划(2019YFD1101005)National Key R & D Program of China(No.2019YFD1101005) （2019YFD1101005）
