An Effective Method of Wood Crack Trace and Quantity Detection Based on Digital Image Processing Technology

Cao, Xinning; Li, Guangming

doi:10.1145/3457682.3457728

Search citation statements

Order By: Relevance

Paper Sections

Select...

Research Status Of Wood Nondestructive Testing1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Other3

Article2

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 8 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e accuracy rate reached 93.3%. e authors of [24] proposed an Otsu method, which used the weighted variance threshold method to detect wood surface defects.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…e accuracy rate reached 93.3%. e authors of [24] proposed an Otsu method, which used the weighted variance threshold method to detect wood surface defects.…”

Section: Research Status Of Wood Nondestructive Testingmentioning

confidence: 99%