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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/111813

    Title: A computer vision assisted system for autonomous forklift vehicles in real factory environment
    Authors: Jia-Liang Syu;Hsin-Ting Li;Jen-Shiun Chiang;Chih-Hsien Hsia*;Po-Han Wu;Chi-Fang Hsieh;Shih-An Li
    Keywords: Industry 4.0;Automated storage and retrieval systems;Forklift;Adaboost;Pallet detection
    Date: 2017-09
    Issue Date: 2017-10-25 02:10:27 (UTC+8)
    Publisher: Springer
    Abstract: 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 %.
    Relation: Multimedia Tools and Applications 76(18), p.18387-18407
    DOI: 10.1007/s11042-016-4123-6
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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