Laser & Optoelectronics Progress, Volume. 56, Issue 10, 101009(2019)
Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network
In this study, a classification method of chopped strand mat defects based on convolutional neural network is proposed. In the proposed method, the rotation, translation, and inversion are employed to expand the dataset for solving the overfitting problem caused by the small data samples in the deep convolutional neural networks. Transfer learning is employed to improve the convergence speed and generalization ability of the network. Further, the different network structures are compared, and the most optimal network structure is used to verify the database. The experimental results demonstrate that the proposed method can effectively classify the chopped strand mat defects with an accuracy rate of 93%.
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Dong Zhuo, Junfeng Jing, Huanhuan Zhang, Zebin Su. Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101009
Category: Image Processing
Received: Oct. 29, 2018
Accepted: Dec. 14, 2018
Published Online: Jul. 4, 2019
The Author Email: Jing Junfeng (jingjunfeng0718@sina.com)