• 论文与研究报告 •

### 基于随机森林算法的纤维板表面缺陷识别

1. 1. 山东建筑大学信息与电气工程学院 济南 250101;
2. 中国林业科学研究院木材工业研究所 北京 100091
• 收稿日期:2018-04-02 修回日期:2018-08-30 出版日期:2018-11-25 发布日期:2018-12-04
• 基金资助:
中国林业科学研究院中央级公益性科研院所基本科研业务费专项资金（CAFYBB2018MB002）；泰山学者优势特色学科人才团队（2015162）。

### Surface Defect Recognition of Fibreboard Based on Random Forest

Liu Chuanze1, Wang Xiao2, Chen Longxian1, Guo Hui2, Luo Rui1, Zhou Yucheng1

1. 1. College of Information and Electrical Engineering, Shandong Jianzhu University Jinan 250101;
2. Research Institute of Wood Industry, CAF Beijing 100091
• Received:2018-04-02 Revised:2018-08-30 Online:2018-11-25 Published:2018-12-04

Abstract: [Objective] In order to satisfy the surface defect recognition quickly with high accuracy, a classification model based on random forest(RF) algorithm is proposed in this study, which can be used for identifying the big shavings, glue spots, debris, oil pollution quickly and accurately on the surface of fibreboard.[Method] Obtaining 100 surface defect images of 4 800 mm×2 400 mm fibreboard, using the Otsu algorithm to realize image segmentation, features including area(S), length(L), the length-width tatio(OR), compactness(J), rectanglarity(P), circularity(O), mean value(u), standard deviation(σD), smoothness(σP),skewness(σS), kurtosis(σK) and root mean square value(σR) of defect area were extracted, these were used as the experimental data. The experimental data were used for constructing RF classifier, choosing 2/3 data and eight features randomly by bootstrap method to construct k decision trees, the RF classifier were consist of these trees. The number of k is determined by calculating the outside data of the bag(OOB) error rate. 100 fibreboards with the wood shavings, glue spots, debris, and oil pollution were used to test by RF classifier.[Result] While k=600, the lowest average OOB error rate of the RF classifier is 0.004, the recognition accuracy of test is 99%, and the recognition time of one fibreboard is 525 ms. Therefore, the RF classifier is superior to NN and SVM on recognition time and accuracy.[Conclusion] The study proves the classifier based on RF algorithm is more feasibility and superiority on defect identification of fibreboard surface, it can achieve surface defect recognition quickly with high accuracy, satisfy the needs of on-line defect detection system of fibreboard.