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
- Laser & Optoelectronics Progress
- Vol. 58, Issue 20, 2010015 (2021)
Fig. 1. Process of maximum pooling calculation
Fig. 2. Structure of CNN13
Fig. 3. Schematic of sand and gravel aggregate. (a) Sand and gravel aggregate with dry surface; (b) sand and gravel aggregate with wet surface
Fig. 4. Schematic of all grades of sand and gravel aggregate. (a) 1st level; (b) 2nd level; (c) 3rd level; (d) 4th level; (e) 5th level
Fig. 5. Comparison curves of loss function between CNN13 model and VGG16 model
Fig. 6. Comparison curves of accuracy between CNN13 model and VGG16 model
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Table 1. Parameter settings of each network layer in CNN13
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Table 2. Classification standard of sand and gravel aggregate
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Table 3. Comparison of original image before and after preprocessing
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Table 4. Comparison of memory consumption between CNN13 model and VGG16 model
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Table 5. Accuracy of CNN13 model and VGG16 model
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