博碩士論文 108325014 詳細資訊

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姓名 紀貞喜(KEE-CHEN HEE)  查詢紙本館藏   畢業系所 土木系營建管理碩士班
論文名稱 運用深度神經網路建立H型鋼構件自動辨識系統之研究
(Establishing recognition system for H-steel components using DNN)
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摘要(中) 建立H型鋼構件的全自動化塗裝產線的第一步便是建立辨識系統。因此,本研究目的在於開發運用深度神經網絡的H型鋼構件自動辨識系統。透過文獻回顧,深度神經網路在辨識方面有著良好的表現。其為所提出的演算法提供核心計算能力,並根據其他8個步驟包括數據的輸入,偵測平面之計算與剔除,對剩餘平面進行分類,計算平行平面可能的間距,確定最佳辨識的構件資訊,對實際和辨識的構件進行比較,輸出至Matplotlib軟件使用所輸出的空間座標資訊建置三維模型,以及完成詳細的三維表面模型建置。透過從塗裝工廠隨機獲得的115件模擬案例和99件實際案例進行驗證, H型鋼構件整體的辨識準確率均高於99.12%,其中大部分的辨識率達到100%,而這些部分包括H型鋼構件的寬度,腹板厚度和翼緣板厚度。本研究的結果表明所提出的系統是可靠的,其平均準確率為99.73%,並且可被實際應用。
摘要(英) The first step to establish a fully automatic coating production line for H-steel components is to set up a recognition system. Therefore, the research purpose is to develop a recognition system for H-steel components using Deep Neural Networks (DNN). Literature review suggests that DNN performs well in recognition. It gives the proposed algorithm the core computation, followed by the other 8 steps of data input, calculation for detected area, classification for residual area, possible distances among parallels, determination for optimal recognition, comparison between actual and recognized components, output to Matplotlib software, 3D plot using output coordinates, and completion of detailed 3D plot. The evaluation is carried out using 115 simulated and 99 actual cases randomly obtained from the factories. The accuracy rates for all parts of H-steel components are higher than 99.12% where 100% recognition rate is reached for most parts such as H-steel width, base plate thickness, and wing plate thickness. The findings support that the proposed system is reliable with an average accuracy rate at 99.73% and applicable in reality.
關鍵字(中) ★ 模式識別
★ H型鋼構件
★ 深度神經網路
★ 自動偵測
關鍵字(英) ★ pattern recognition
★ H-steel component
★ Deep Neural Networks (DNN)
★ automatic detection
論文目次 摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章、緒論 1
1.1研究背景與動機 1
1.2 研究問題 2
1.3 研究目的 2
1.4 研究範圍 2
1.5 研究流程 3
第二章、文獻回顧 6
2.1 表面辨識 6
2.2 模式識別於土木領域的應用 7
2.3 深度神經網路 7
2.4 自動化處理相關的表面資訊獲取方式 8
2.4.1 CAD模型取得表面資訊 8
2.4.2 圖像或影像辨識取得表面資訊 9
2.4.3 掃描方式取得表面資訊 9
2.5 立體光刻模型檔案 10
2.6 過往H型鋼構件之辨識成果 11
2.7 鋼構件塗裝 11
第三章、H型鋼構件自動辨識系統 13
3.1 研究假設 13
3.2 H型鋼構件STL模型表面辨識演算法 14
3.3 尺寸參數取得 16
3.4 塗裝表面輪廓模型建置 21
3.5 模型驗證 22
第四章、驗證結果分析與討論 27
4.1 特殊情況探討 28
4.2 綜合分析與討論 33
第五章、結論與建議 36
5.1 結論 36
5.2 後續研究建議 37
參考文獻 38
附錄一、尺寸參數獲取結果 41
附錄二、尺寸參數辨識率 48
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指導教授 陳介豪(Jieh-Haur Chen) 審核日期 2021-1-26
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