Abstract
Wood recognition is necessary for work in the wood trade activities. The advantage of the one-class wood classification method is more generalization, and it only needs positive samples and does not need negative samples in the training phase, so it is suitable for rare wood species inspection. This paper proposed an improved method based on the one-class support vector machine (OCSVM) for wood species recognition. It uses cross-section images acquired with a magnifying glass, which uses a pre-trained VGG16 model for feature extraction, a normal distribution test for key features filtering, and OCSVM to determine the wood species. The results showed that the approach achieved a mean recall of 0.842 for both positive and negative samples, which indicates this method has good performance for wood recognition. In a negative public dataset, the negative recall reached as high as 0.989, which showed that this method has good generalization.
Funder
National Natural Science Foundation of China
Major Project of Science and Technology of Yunnan Province
Reference45 articles.
1. Machine vision for field-level wood identification
2. kNN: Classification of Agarwood Types in Oil and Wooden Using E-nose;Mohamad;Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering,2021
3. Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection
4. A new Gabor based approach for wood recognition
5. Very deep convolutional networks for large-scale image recognition;Simonyan;arXiv,2014
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献