文献类型： 外文期刊
作者： Liu, Tian-Hu 1 ; Wu, Zi-Di 1 ; Chen, Qin-Ling 2 ; Nie, Xiang-Ning 1 ; Li, Gui-Qi 1 ; Wang, Hong-Jun 1 ; Zhang, Di 1 ; Liu, Wei 1 ; Wu, Jin-Meng 1 ;
作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou, Guangdong, Peoples R China
2.Guangdong Acad Agr Sci, Guangzhou, Guangdong, Peoples R China
期刊名称：FOREST PRODUCTS JOURNAL （ 影响因子：0.968； 五年影响因子：0.93 ）
ISSN： 0015-7473
年卷期： 2021 年 71 卷 1 期
页码：
收录情况： SCI
摘要： The defect rate of initially produced block bamboo (Bambusoideae) parts is >20 percent. Sorting out these defective parts manually is a highly time-consuming and tedious process. An intelligent sorting system was developed based on machine vision using a Radial Basis Function (RBF) neural network learning algorithm in this study. First, a high-speed charge-coupled device camera was used to obtain a series of images of perfect and defective block bamboo parts. Next, the RBF neural-network learning algorithm was applied to obtain defect characteristics and to locate defective parts moving forward on a conveyor belt. An array of air jets was designed to force defective parts off the belt. Experimental results showed that the average defective part removal rate of the proposed system was 91.7 percent.
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