Laser & Optoelectronics Progress, Volume. 58, Issue 20, 2010015(2021)
Research on Classification Method of Sand and Gravel Aggregate Based on Convolutional Neural Network
In the current process of detecting commercial sand and gravel aggregates, the manual detection is inefficient, greatly affected by subjective factors, and the detection accuracy is not ideal. This paper proposes a convolutional neural network (CNN) based on the sand and gravel aggregate image classification model (CNN13). This classification model refers to the classic CNN visual geometry group 16 (VGG16) model to improve the network structure and optimize parameters. The CNN13 model uses the TensorFlow deep learning framework to build a 13-layer CNN structure. The experimental dataset includes 5000 digital images, which is collected from the sand and gravel aggregates in the daily production of a commercial concrete manufacturer. The model uses GPU for high-speed calculation during the training process. Compared with the VGG16 model, the CNN13 model has fewer convolutional layers and parameters, lower requirements for GPU memory, faster training speed, higher classification accuracy, and classification accuracy for each level of sand and gravel aggregates is more than 99%.
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Ran Yan, Jideng Liao, Xiaoyong Wu, Changjiang Xie, Lei Xia. Research on Classification Method of Sand and Gravel Aggregate Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2010015
Category: Image Processing
Received: Nov. 28, 2020
Accepted: Jan. 11, 2021
Published Online: Oct. 13, 2021
The Author Email: Yan Ran (yanran@cqut.edu.cn)