1. Intelligent Bamboo Part Sorting System Design via Machine Vision
- Author
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Liu, Tian-Hu, Li, Gui-Qi, Wu, Zi-Di, Wang, Hong-Jun, Wu, Jin-Meng, Chen, Qin-Ling, Zhang, Di, Nie, Xiang-Ning, and Liu, Wei
- Subjects
Sorting devices -- Design and construction -- Technology application ,Bamboo -- Production processes ,Machine vision -- Usage ,Technology application ,Business ,Forest products industry - Abstract
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., Bamboo (Bambusoideae) is commonly distributed throughout the Asia-Pacific region, Americas, and Africa, as shown in Figure 1. In Asia, bamboo plants are mainly distributed in China, India, and other developing [...]
- Published
- 2021
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