Journal of System Simulation
Abstract
Abstract: In order to obtain the location information and category information of wood surface defects in the early stage of intelligent wood cutting, a combined algorithm for fast identification and location of wood surface defects is proposed based on digital image processing technology for fast recognition and location of wood surface defects. The Adaboost cascade classifier algorithm is used to extract the candidate frames of the wood surface defect region in the image in order to solve the problem that the traditional segmentation method is not effective for multi objects processing. A CNN model with self-learning ability is used to classify the input candidate boxes, so as to overcome the difficulty of selecting features in traditional classification methods. 200 multi-target samples are selected for testing, and the results show that the recall rate is 94%, the accuracy of detection is 99%, and the accuracy of classification is 97.9%. The experimental results show that the algorithm can meet the requirements of localization and classification of wood surface defects.
Recommended Citation
Wang, Hongjun; Li, Zouzou; and Zou, Xiangjun
(2019)
"Wood Surface Defect Detection Based on Adaboost and CNN,"
Journal of System Simulation: Vol. 31:
Iss.
8, Article 17.
DOI: 10.16182/j.issn1004731x.joss.17-0262
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol31/iss8/17
First Page
1636
Revised Date
2017-07-10
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.17-0262
Last Page
1645
CLC
TP391.41
Recommended Citation
Wang Hongjun, Li Zouzou, Zou Xiangjun. Wood Surface Defect Detection Based on Adaboost and CNN[J]. Journal of System Simulation, 2019, 31(8): 1636-1645.
DOI
10.16182/j.issn1004731x.joss.17-0262
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