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研究生:王辰軒
研究生(外文):Chen-Hsuan Wang
論文名稱:SEMA: 工地車輛管控助理
論文名稱(外文):SEMA: A Site Equipment Management Assistant for Progress Management
指導教授:楊亦東楊亦東引用關係蔡孟涵蔡孟涵引用關係
指導教授(外文):I-Tung YangMeng-Han Tsai
口試委員:康仕仲楊亦東蔡孟涵
口試委員(外文):Shih-Chung KangI-Tung YangMeng-Han Tsai
口試日期:2020-07-31
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:營建工程系
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:76
中文關鍵詞:YOLOv3聊天機器人深度學習人工智慧影像辨識即時通訊軟體光學字元識別
外文關鍵詞:YOLOv3ChatbotDeep-learningArtificial IntelligenceImage recognitionInstant messaging applicationOptical Character Recognition
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在營建業進度管理中,搜集並記錄工程設備資訊是很重要的任務之一,大多數建築施工現場都使用紙本紀錄將工程設備的進出資訊記錄下來。然而,手動紀錄資訊是一件高勞力且耗時的任務。因此,如何自動化監視並且記錄成為進度管理中的關鍵挑戰。本研究開發了一種用於工程設備且具有影像辨識以及多目標追蹤的進度管理系統,名為Site Equipment Management Assistant (SEMA) 該系統可以從施工的監控設備中擷取與工程設備相關的資訊,並且轉為結構化資訊記錄下來。該系統包含七個模組:資訊採集模組,工程設備追蹤模組,濃縮影片擷取模組,主動通知模組,工程設備通行單轉換模組,用戶意圖分析模組以及用戶界面模組。我們訓練了一個可以即時自動檢測並追蹤工程設備的模型。使用此模型,我們可以立即辨識出經過施工現場監控系統的工程設備,並及時通知相關人員。該系統具有儲存工程設備相關資訊的資料庫,可以讓用戶通過具有直觀介面且容易上手的聊天機器人經由關鍵字獲得工程設備相關資訊。根據系統評估結果顯示,該系統的平均精度均值以及召回率分別為87.14%以及69%。該系統也通過使用者測試進行了可用性測試,通過可用性測試結果,驗證該系統於使用者查詢工程設備相關資訊時能夠比紙本傳統作業平均節省718.7秒的時間。通過上述評估以及測試結果,該系統可以有效地提高紀錄以及獲得工程設備相關資訊的效率,對於施工現場的進度管理有顯著的改善。
Collecting construction equipment information such as the site equipment enter and exit date-time, driver's name, type, and quantity is one of the most essential tasks in project progress management. Most construction projects use paper to record the equipment access history. However, manual recording is always labor-intensive and time-consuming. Therefore, a critical challenge in site progress management is how to automate this manual processing. This study develops a construction site equipment management system with image recognition and multiple object tracking for construction equipment management. The system can extract the equipment-related information from construction site video and convert it into structured data. The system contains seven modules: data acquisition, construction equipment tracking, highlight video extraction, proactive notification, access form detection, user intent analysis, and user interface. We trained a model that can recognize construction equipment automatically in real-time. Using this model, we can instantly recognize the construction equipment passing by the site monitor and notify the relevant personnel. The system also has a construction equipment database to store data. Users can obtain data through a chatbot with an intuitive and easy-to-use interface. Evaluation results showed that the mean average precision and recall of the system for recognizing construction equipment achieves are 87% and 69%, respectively. Through usability testing, the system was validated to be able to save users 718.7 seconds on average for querying construction equipment related information. The results of the usability test showed that the system can effectively improve record efficiency for project progress management and save user time spent querying data.
Table of contents V
List of Figures VII
List of Tables X
1. Introduction 1
2. Literature review 3
2.1 Videos in construction management 5
2.2 Construction resource recognition 7
2.3 Challenges of data delivery 10
2.4 Chatbot for data transmission 11
3. Objective 12
4. Methodology 13
4.1 System architecture 13
4.2 Construction equipment tracking module 15
4.2.1 Multiple object tracking 19
4.3 The access form detection module 24
4.4 Highlight video extraction module 24
4.5 User intent analysis module 27
4.6 User interface modules 28
5. Implementation 29
5.1 Dataset preparation and training 30
5.2 Video processing 33
5.3 Chatbot design 36
5.4 Key functions of SEMA 37
5.4.1 Daily report 38
5.4.2 Proactive notification 39
5.4.3 Live stream 40
5.4.4 Access form 40
5.4.5 Weekly report 43
5.4.6 Highlight video 44
5.4.7 Schedule setting 45ㄅ
6. Validation 47
6.1 System evaluation 47
6.2 Usability test 50
6.2.1 Test design 50
6.2.2 Results 53
7. Discussion 57
7.1. Contributions 57
7.2. Future work 59
7.3 Potential Applications of the SEMA 60
8. Conclusion 61
Reference 63
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