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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 謝尚賢(Shang-Hsien Hsieh) | |
| dc.contributor.author | Zi-Hao Lin | en |
| dc.contributor.author | 林子皓 | zh_TW |
| dc.date.accessioned | 2021-06-16T06:56:02Z | - |
| dc.date.available | 2020-08-21 | |
| dc.date.copyright | 2020-08-21 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-14 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57650 | - |
| dc.description.abstract | 對營造業而言，增加利潤、降低成本是首要目標，意味著維持施工順暢極其重要。異常施工活動不僅容易拖延進度，且與人員安全息息相關。因此，本研究提出時序性圖像之分析方法，藉此辨識潛在異常活動，並視覺化呈現施工過程。分析流程共可分為四個模組：物件偵測、物件追蹤、行為辨識、施工活動分析。物件偵測模組採用遷移式學習訓練辨識模型，於影像中辨識工地人員及重機具，再透過物件追蹤模組匹配辨識到的物件。接著，行為辨識模組利用物件追蹤結果辨識出人員及重機具所執行的動作，再根據時間軸繪製成折線圖。施工活動分析模組採用統計理論從所有動作週期中過濾出潛在異常活動，並於折線圖中標註異常區塊。
本研究以實際於工地拍攝之開挖作業影片進行驗證，物件辨識模組達到70.30%之精度，物件追蹤模組達到82.12%之準確度。施工活動分析模組不僅辨識出影片中的潛在異常活動，亦過濾出卡車交班。為達到實際應用效益，該模組統計出施工過程各式資訊，同時標註潛在異常事件之起終時間，方便使用者回顧監視影像。透過折線圖紀錄並分析施工活動，工地主任將能快速回顧整個施工過程，探討異常事件發生原因，進而維持順暢施工，確保人員安全。 | zh_TW |
| dc.description.abstract | To increase profit, ensuring project schedule is the primary objective for the construction industry. Abnormal activities may reduce productivity or even cause accidents. Therefore, this study adopts image analytics approach to automatically identify potential abnormal events and visualize construction process. The pipeline consists of four modules: object detection, object tracking, action recognition, and operational analysis. At first, Faster Region-proposal Convolutional Neural Network (Faster R-CNN) is adopted to detect workers and heavy equipment on the construction jobsites, and Simple Online and Realtime Tracking (SORT) algorithm is improved to associate detected objects between images. Afterwards, a hybrid model integrating CNN and Long Short Term Memory (LSTM) is employed for the purpose of action recognition. The results are documented into a line chart form of Crew-balance Chart (CBC) to visualize the construction process, in which irregular operations are identified through statistical theory. The approaches were validated with the videos of earthmoving operation. The Average Precision (AP) of the trained detector is about 70.31%, and the Multiple Object Tracking Accuracy (MOTA) of the modified SORT is around 82.12%. In the testing videos, not only potential abnormal activities were pre-screened, but truck exchanges were filtered. Meanwhile, an activity log with operational information and starting and ending times of irregular events was created. Through the line chart form of CBC and activity log provided by the image analytics approach, potential abnormal events can be deeply investigated for further enhancement. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T06:56:02Z (GMT). No. of bitstreams: 1 U0001-1707202023351800.pdf: 9579429 bytes, checksum: 9102b1b2eec505160ca948c7e2212801 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Acknowledgements i 中文摘要 ii Abstract iii Contents v List of Figures vii List of Tables x Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Objective 2 1.3 Organization of Thesis 3 Chapter 2 Literature Review 4 2.1 Construction Object Detection 4 2.2 Construction Object Tracking 7 2.3 Construction Action Recognition 8 2.4 Image Analytics of Construction Process 10 2.5 Summary 13 Chapter 3 Image Analytics Approach 14 3.1 Object Detection 14 3.2 Object Tracking 16 3.2.1 SORT Algorithm 17 3.2.2 DeepSORT Algorithm 18 3.3 Action Recognition 20 3.4 Operational Analysis 22 3.4.1 Gaussian Distribution Theory 23 3.4.2 Box Plot Theory 24 3.4.3 Line Chart Form of CBC 25 Chapter 4 Implementation, Validation and Application 26 4.1 Heavy Equipment Detection 26 4.1.1 Implementation Details 26 4.1.2 Detection Results 28 4.2 Heavy Equipment Tracking 33 4.2.1 Implementation Details 33 4.2.2 Tracking Results 33 4.3 Operational Analysis 39 4.3.1 Normality Test Results 40 4.3.2 Identification of Potential Abnormal Construction Activities 41 4.3.3 Practical Applications 52 Chapter 5 Conclusion and Recommendations 57 5.1 Conclusion 57 5.2 Recommendations 58 References 60 | |
| dc.language.iso | en | |
| dc.subject | 物件追蹤 | zh_TW |
| dc.subject | 施工活動追蹤 | zh_TW |
| dc.subject | 物件偵測 | zh_TW |
| dc.subject | 生產力分析 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | Activity Tracking | en |
| dc.subject | Productivity | en |
| dc.subject | Deep Learning | en |
| dc.subject | Object Detection | en |
| dc.subject | Object Tracking | en |
| dc.title | 以圖像分析方法辨識潛在異常施工活動 | zh_TW |
| dc.title | Identification of Potential Abnormal Construction Activities Using Image Analytics | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 陳柏華(Albert Y. Chen) | |
| dc.contributor.oralexamcommittee | 陳柏翰(Po-Han Chen),周建成(Chien-Cheng Chou) | |
| dc.subject.keyword | 施工活動追蹤,生產力分析,深度學習,物件偵測,物件追蹤, | zh_TW |
| dc.subject.keyword | Activity Tracking,Productivity,Deep Learning,Object Detection,Object Tracking, | en |
| dc.relation.page | 73 | |
| dc.identifier.doi | 10.6342/NTU202001611 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-14 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
| Appears in Collections: | 土木工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| U0001-1707202023351800.pdf Restricted Access | 9.35 MB | Adobe PDF |
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