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請使用永久網址來引用或連結此文件:
https://irlib.pccu.edu.tw/handle/987654321/39364
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| 題名: | A computer vision assisted system for autonomous forklift vehicles in real factory environment |
| 作者: | Syu, JL (Syu, Jia-Liang)
Li, HT (Li, Hsin-Ting)
Chiang, JS (Chiang, Jen-Shiun)
Hsia, CH (Hsia, Chih-Hsien)
Wu, PH (Wu, Po-Han)
Hsieh, CF (Hsieh, Chi-Fang)
Li, SA (Li, Shih-An) |
| 貢獻者: | 電機系 |
| 關鍵詞: | Industry 4.0
Automated storage and retrieval systems
Forklift;Adaboost
Pallet detection |
| 日期: | 2017-09 |
| 上傳時間: | 2018-01-29 10:34:18 (UTC+8) |
| 摘要: | Industry 4.0 is an important trend in factory automation nowadays. Among the Automated-Storage-and-Retrieval-System (ASRS) is one of the most important issues for industry. It is widely used in a variety of industries for a variety of storage applications in factories and warehouses. However, the cost of constructing an ASRS is so high that most small/medium enterprises cannot afford it. A forklift system is a cheaper alternative to a complicated ASRS. In this work, a new pallet detection method that uses an Adaptive Structure Feature (ASF) and Direction Weighted Overlapping (DWO) ratio to allow forklifts to pick up a pallet is proposed, using a monocular vision system on the forklift. Combining the ASF and DWO ratio for pallet detection, the proposed method removes most of the non-stationary (dynamic) background and significantly increases the processing efficiency. A Haar like-based Adaboost scheme uses an AS for pallets algorithm to detect pallets. It detects the pallet in a dark environment. Finally, by calculating the DWO ratio between the detected pallets and tracking records, it avoids erroneous candidates during object tracking. Therefore, this work improves the pallet detection to solve the problem with an effective design. As results show that the hybrid algorithms that are proposed in this work increase the average pallet detection rate by 95 %. |
| 關聯: | MULTIMEDIA TOOLS AND APPLICATIONS 卷: 76 期: 18 頁碼: 18387-18407 |
| 顯示於類別: | [電機工程系] 期刊論文
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