Wood Quality of Young Teak in Different Planting Spaces

Amodei, Juliana Bernardo; Latorraca, João Vicente de Figueiredo; Santos, Glaycianne Christine Vieira dos; Martins, Bianca Cerqueira

doi:10.1590/2179-8087-floram-2021-0026

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…e authors of [18] used principal component analysis to reduce the dimension of extracted features which can effectively identify and locate defects of complex inner holes. e authors of [19] input five features including the first three features of local binary mode and Tamura texture together with the entropy feature of gray co-occurrence matrix into the classifier of the support vector machine, and the recognition accuracy is 91.67%, which is better than that of BP neural network classifier, and the recognition accuracy of BP neural network is 82.75%. e authors of [20] used the improved Grab Cuts algorithm for optimization to solve the problems of the image under segmentation and easy interference by regional texture in the traditional algorithm.…”

Section: Research Status Of Wood Nondestructive Testingmentioning

confidence: 99%

Wood Quality Defect Detection Based on Deep Learning and Multicriteria Framework

Ping-an

2022

Mathematical Problems in Engineering

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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.

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Section: Research Status Of Wood Nondestructive Testingmentioning

confidence: 99%