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• W4281694844 abstract "Traditional nondestructive testing technology for wood defects has a series of problems such as low identification accuracy, high cost, and cumbersome operation, and traditional testing methods cannot accurately show the specific location and size of wood internal defects; it is urgent to explore a new nondestructive testing scheme for wood defects. Aiming at this problem, this paper designs and develops an automatic detection method for wood surface defects based on deep learning algorithm and multicriteria framework. By comparing the performance of different deep learning detection methods on the data set, the advantages and disadvantages of the detection method in this paper are proved. After a series of works, such as the development and optimization of the experimental algorithm, the algorithm proposed meets the requirements in both the detection accuracy and training time." @default.
• W4281694844 created "2022-06-13" @default.
• W4281694844 creator A5035746776 @default.
• W4281694844 date "2022-05-26" @default.
• W4281694844 modified "2023-09-30" @default.
• W4281694844 title "Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework" @default.
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• W4281694844 doi "https://doi.org/10.1155/2022/4878090" @default.
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