可適性結構演算法應用於輔助堆高機棧板檢測

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 學位論文 > Item 987654321/111458

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/111458

Title:	可適性結構演算法應用於輔助堆高機棧板檢測
Other Titles:	An assisted forklift pallet detection with adaptive structure algorithm
Authors:	徐嘉良;Syu, Jia-Liang
Contributors:	淡江大學電機工程學系碩士班江正雄;Chiang, Jen-Shiun
Keywords:	工業4.0;自動倉儲系統;堆高機;棧板檢測;industry 4.0;Automated storage and retrieval systems;Forklift;Adaboost;Pallet detection
Date:	2016
Issue Date:	2017-08-24 23:53:20 (UTC+8)
Abstract:	工業4.0為現今工廠自動化的重要趨勢，自動倉儲系統(Automated Storage and Retrieval System, ASRS)更是其中較重要的議題，它被廣泛地應用在各種類型的工廠中去處理各種不同的儲存應用。然而建造一套完整的自動倉儲系統的花費過於高昂，普遍的中小企業並沒有辦法負擔得起。而AGV(Automatically Guided Vehicle)則是一個低成本並且可以替代部分自動倉儲系統的方案，通過在堆高機上安裝AGV系統，便能夠達到自動化運輸的目的，在其中關鍵則是必須知道棧板的位置。因此本論文提出一個基於自適應結構特徵(Adaptive Structure Feature, AS)與方向加權重疊率(Direction Weighted Overlapping Ratio, DWO)的棧板辨識系統，用以輔助堆高機的取放貨。透過所提出的自適應結構特徵與方向加權重疊率結合棧板檢測，可以移除大多數非棧板的背景，並且增加處理效率。在我們的方法中應用了Haar-like特徵，結合Adaboost與階層式分類器的架構來檢測棧板，並使用變異數特徵與自適應結構特徵來優化檢測結果。最後計算各候選區與追蹤紀錄的方向加權重疊率來進行非最大抑制，以選出最適合的結果。實驗結果證明我們的方法，可以在光線較少的環境下檢測出棧板，同時這樣的混合式檢測系統，對於棧板的檢測率可以達到平均95%。 Nowadays, Industry 4.0 is an important trend in factory automation (FA), which automated-storage-and-retrieval-system (ASRS) is one of the most important issues in the industry. It has been widely used in a variety of industries to handle a variety of storage applications in factories, warehouses, etc. However, the cost to construct an ASRS is too expensive such that most of the small/medium enterprises cannot afford it. A forklift system is another alternative solution to replace the complicated ASRS due to its low cost characteristics. In this work, a new pallet detection based on adaptive structure feature (AS) and direction weighted overlapping ratio (DWO) to aid forklifts in picking up the pallet process model is proposed from a single-lens camera erected at the forklift. Combining AS and DWO for pallet detection, the proposed method can remove most of the non-stationary (dynamic) background and significantly increase the processing efficiency. In our approach, Haar like-based Adaboost scheme with AS of pallets algorithm to detect pallets is presented. It can detect the pallet at luminance less environment. Finally, by calculating the DWO between the detected pallets and tracking record, it can avoid those error responses in object tracking. Therefore, this work improves the pallet detection to solve the problem with an effective design. As results, the hybrid algorithms proposed in this work can increase the average pallet detection rate by 95%.
Appears in Collections:	[電機工程學系暨研究所] 學位論文

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