3.4. 参考文献#

待处理

正确的文献引用格式

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[2]Silva W R L, Lucena D S. Concrete cracks detection based on deep learning image classification[C]//Proceedings. MDPI, 2018, 2(8): 489.

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[5]Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 1-9.

[6]He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.

[7]Deng J, Dong W, Socher R, et al. Imagenet: A large-scale hierarchical image database[C]//2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009: 248-255.

[8]Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International conference on machine learning. pmlr, 2015: 448-456.

[9]Kaggleh Official Website. Download - Datasets[EB/OL].https://www.kaggle.com/datasets/aniruddhsharma/structural-defects-network-concrete-crack-images, 2022-1-10/2023-01-30.

[10]Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. The journal of machine learning research, 2014, 15(1): 1929-1958.

[11]Prechelt L. Early stopping—but when?[J]. Neural networks: tricks of the trade: second edition, 2012: 53-67.