Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry

CCUR > College of Natural Science and Engineering > College of Engineering > Department of Computer Science and Information Engineering > journal articles > Item 987654321/45491

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/45491

Title:	Deep-Learning-Based Defective Bean Inspection with GAN-Structured Automated Labeled Data Augmentation in Coffee Industry
Authors:	Chou, YC (Chou, Yung-Chien) Kuo, CJ (Kuo, Cheng-Ju) Chen, TT (Chen, Tzu-Ting) Horng, GJ (Horng, Gwo-Jiun) Pai, MY (Pai, Mao-Yuan) Wu, ME (Wu, Mu-En) Lin, YC (Lin, Yu-Chuan) Hung, MH (Hung, Min-Hsiung) Su, WT (Su, Wei-Tsung) Chen, YC (Chen, Yi-Chung) Wang, DC (Wang, Ding-Chau) Chen, HC (Chen, Hao-Chun)
Contributors:	資工系
Keywords:	automatic defect inspection machine learning smart agriculture automation engineering data augmentation applied artificial intelligence GAN optimizer
Date:	2019-10
Issue Date:	2019-12-16 13:49:21 (UTC+8)
Abstract:	In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to
Relation:	APPLIED SCIENCES-BASEL 卷冊: 9 期: 19
Appears in Collections:	[Department of Computer Science and Information Engineering] journal articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	732	View/Open

View Licence

Loading...