基于卷积神经网络的预制叠合板多目标智能化检测方法OACSTPCD

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.