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系統識別號 U0026-2703201923195100
論文名稱(中文) 於嵌入式系統實現瓶罐挑選-使用機器手臂與深度學習之設計與實作
論文名稱(英文) Design and Implementation of an Embedded System for Picking up Bottles Using an Robotic Arm and Deep Learning
校院名稱 成功大學
系所名稱(中) 工程科學系碩士在職專班
系所名稱(英) Department of Engineering Science (on the job class)
學年度 107
學期 1
出版年 108
研究生(中文) 葉千暉
研究生(英文) Chien-Hui Yeh
學號 N97051044
學位類別 碩士
語文別 中文
論文頁數 65頁
口試委員 指導教授-侯廷偉
召集委員-席家年
口試委員-楊竹星
口試委員-林志敏
口試委員-鄧維光
中文關鍵字 嵌入式系統  神經網路  影像辨識  機械手臂 
英文關鍵字 Embedded System  Neural Network  Image Recognition  Robotic Arm 
學科別分類
中文摘要 本論文針對可資源回收的瓶罐,透過嵌入式系統搭配深度學習影像辨識模型和機械手臂,實作一套可攜式瓶罐(含寶特瓶、易開罐、鋁箔包)挑選系統,並使用電動旋轉台模擬輸送帶的動態環境。影像辦識模型則採用了AlexNet和SSD深度神經網路為基礎,並加以修改為適合嵌入式系統使用的輕量化神經網路模型,名為AlexNet-SSD。
在網路模型訓練上,自行拍攝和收集了共約1200張相關瓶罐照片的資料集,隨機選擇1000張為訓練集而另外200張為測試集。最後將在桌上型電腦主機訓練完成的AlexNet-SSD神經網路模型,移植到嵌入式系統上進行測試。辨識的速度可高達26.8 FPS且凖確率為87.3%。
英文摘要 As people's awareness of environmental protection increases, improving resource reuse is a way to effectively reduce environmental pollution and achieve sustainable use of resources. But waste disposal is a complex process that requires a complex checking process before it enters the incineration. In addition, a large amount of human resources is required to pre-classify waste. The pre-classifying process greatly increases cost, reduces the efficiency, and even directly leads to abandon the classification action and directly incinerates.
In this research, we propose an automatic system for picking up bottles based on an embedded system with a deep learning model and a robotic arm. In order to make the system practical, the research used a rotating table to simulate a conveyor belt, and combined with a camera and image recognition. The image recognition model is based on the neural network architecture composed of AlexNet and SSD, and it is modified to be a lightweight neural network model for embedded system called AlexNet-SSD.
In the model training phase, nearly 1200 images of PET bottles, can, and tetra-pak were collected. Among which 1000 pieces were used as the training set and 200 pieces as the test set. Finally, the training results on the host are ported to the embedded system for testing. The testing results are 1) recognition speed can be as high as 26.8 frames per second, and 2) an accuracy of 87.3%.
論文目次 中文摘要 I
Extended Abstract II
誌謝 VIII
章節目錄 IX
表目錄 XI
圖目錄 XII
第一章 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 1
1-3 研究貢獻 2
1-4 論文架構 3
第二章 文獻探討 4
2-1 台灣廢棄物種類認知 4
2-2 廢棄物辨識相關研究 5
2-3 各國自動化廢棄物分類技術 12
2-4 自動化分類技術相關比較 14
第三章 嵌入式系統實現瓶罐挑選 15
3-1 實驗場景介紹 15
3-2 硬體設備 17
3-3 系統架構 21
3-4 影像辨識演算法說明 24
3-5 機械手臂和坐標定位說明 33
第四章 實作成果與討論 37
4-1 AlexNet-SSD神經網路模型訓練結果 37
4-2 瓶罐影像辨識實驗結果 38
4-3 機械手臂吸取實驗結果 42
4-4 討論 43
第五章 結論與未來工作 46
5-1 結論 46
5-2 未來工作 47
參考文獻 48
附錄 52
附錄1 uArm Swift SPEC 52
附錄2 系統程式碼 52
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