智慧型計算結合深度攝影影像應用於土方 空拍測量之研究

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姓名

錢冠宇(Kuan-Yu Chien) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

智慧型計算結合深度攝影影像應用於土方空拍測量之研究
(Developing computational intelligence-enhanced depth sensing detection on earthwork management)

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至系統瀏覽論文 (2026-2-1以後開放)

摘要(中)

土石方之管理需投注大量資源以及人力。每年臺灣有超過 3 億立方公尺的剩餘土石方，相關業者亟需採行有效率之經營以進行妥善管理。本研究旨在發展智慧型計算增強深度傳感檢測，應用於以 U-Net 輔助之土石方管理。回顧之過去文獻主題包含土石方計算、應用無人機技術檢測之可行性、物件偵測、以及 U-Net 之設定。使用無人機於土石方堆置場進行範圍 43,939 平方公尺之實地飛行深度感測。飛行高度為海拔 60 公尺、速度每秒 4.7 公尺， 10 分 51 秒之總飛行時間中總共擷取 276 張高解析度航拍影像，其航程重疊率為 80%，照片重疊率為 70%。經由 U-Net 分析，土石方堆置場檢測之平均準確率達 92.5%。比對 2021 年 10 月至 2022 年 8 月，10 個月之土石進場申報量，本研究建置之模型檢測到該場域實際增加 13.84%體積的土方量，此結果大致符合土方場之實際運作狀況。本研究之成果亦可提供相關業者更具效率之土石方量計算方法。

摘要(英)

Earthwork management requires massive resources and manpower to deal with over 30 million cube meters of earth annually in Taiwan. An efficient solution is desired for practitioners to operate earthwork management in the field. The research goal is to develop a computational ntelligence-enhanced depth sensing detection on earthwork management
using U-Net. The literature review suggests earthwork calculation, Unmanned Aerial Vehicle (UAV) detection feasibility, object detection, and settings for U-Net. Field aerial depth sensing is performed using UAV at a pre-determined earthwork site covering for a 43,939 square meter area. The total UAV airborne time is 10 minutes and 51 seconds at steady 60 meters above sea leave with 4.7m/sec for speed. A total of 276 high-resolution photos are btained with a course overlap rate at 80% and photo overlap rate at 70%. The average accuracy rate for detection on earthwork site yielded from U-Net is 92.5%. With the comparison to the actual earth imported to the field during 10 months 2021/10-
2022/08), the proposed model detects +13.84% incremental volume to the field. The finding that matches empirical operation for an earthwork field can also provide practitioners with an efficient management way for earthwork calculation.

關鍵字(中)

★ 土石方管理
★ 無人機
★ 物件偵測
★ 辨識
★ 智慧型計算

關鍵字(英)

★ Earthwork management
★ Unmanned Aerial Vehicle (UAV)
★ object detection
★ recognition
★ computational intelligence

論文目次

目錄
圖目錄................. V
表目錄.......................... VIII
第一章緒論.................................... 1
1.1 研究動機................................................. 1
1.2 研究問題............................... 3
1.3 研究目的........................................ 5
1.4 研究範圍及研究限制......................... 5
1.5 研究流程............................................. 6
第二章文獻回顧.......................................... 8
2.1 土方測量計算........................... 8
2.1.1 建立控點.................................. 8
2.1.2 土方測量管理....................... 9
2.2 無人機空拍...................................... 10
2.2.1UAV 定位............................ 10
2.2.2 視覺化運用............................. 12
2.2.3 空拍測量限制........................ 13
2.3 演算方法.......................................... 14
2.3.1 探討 Mask R-CNN(Region-based Convolutional Neural Networks) 14
2.3.2 改良方法，提高預期的結果............................ 16
2.3.3 演算方法應用................... 16
2.3.4 找到目標不合理處............... 18
2.3.5 鴿群最佳化演算法................ 19
2.4 物件偵測.......................................... 20
2.4.1 語意分割................................. 21
2.4.2 實例分割................................. 22
2.5 U-net ............................................. 23
第三章數據收集與分析...............................27
3.1 資料蒐集.......................................... 27
3.2 資料蒐集工具.................................. 28
3.3 選擇無人機....................................... 29
3.4 影像擷取(空拍過程與結果)............. 32
第四章影像辨識模式與辨識成果.............36
4.1 影像偵測與物件分類...................... 36
4.2U-Net 影像辨識點雲土方計算模組........................ 38
4.3 影像辨識與計算成果....................... 55
4.4 辨識成果驗證................................. 63
4.5 綜合討論.......................................... 65
第五章結論與建議...................................67
5.1 結論................................................ 67
5.2 建議.............................................. 68
參考文獻.................................................70
圖目錄
圖 1-1 研究流程圖.......................................6
圖 2-1 為 Mask R-CNN 之示意................... 15
圖 2-2 NAS-Une 示意圖.................................. 18
圖 2-3 語意分割示意圖............................... 21
圖 2-4 是實例分割示意圖 .................................. 23
圖 2-5 U-Net 架構之示意圖 .............................. 25
圖 2-6 相關訓練損失之比較 ....................... 25
圖 2-7U-Net, U-Net++, U-Net3+示意圖........... 26
圖 3-1 金門土資場相關規範 .......................... 28
圖 3-2 Phantom 4 RTK 及 D-RTK 2 高精準度 GNSS 度移動站........................... 30
圖 3-3Phantom 4 RTK 無人機 ............... 32
圖 3-4 測量控制點 ..................................... 32
圖 3-5 架設 RTK 之過程(1-3)................... 33
圖 3-6 架設 RTK 之過程(2-3)................. 33
圖 3-7RTK 連線過程(3-3)........................... 33
圖 3-8 無人機參數設定的面板及部分數據(1-3) ................................................... 34
圖 3-9 無人機參數設定的面板及部分數據(2-3) ................................................... 34
圖 3-10 無人機參數設定的面板及部分數據(3-3) ................................................. 35
圖 4-1 空拍二維的平面圖 ..................... 36
圖 4-2 圖片屬性資料 ............................... 36
圖 4-3 LabelMe 程式辨識物件過程(1) .................................... 37
圖 4-4 LabelMe 程式辨識物件過程(2) .................................... 38
圖 4-5 灰階圖 ...................................... 39
圖 4-6 原始圖片 .................................. 40
圖 4-7 Mask 示意圖 ............................... 40
圖 4-8 原始像素數值 ................................. 42
圖 4-9 卷積核 ........................................ 42
圖 4-10 特徵數值 .................................. 43
圖 4-11 卷積處理過程 ......................... 43
圖 4-12ReLU 示意圖......................... 44
圖 4-13 池化過程 .............................. 45
圖 4-14U-Net 架構示意圖 .................... 46
圖 4-15 點雲建模圖(1)..................... 49
圖 4-16 點雲建模圖(2)..................................... 49
圖 4-17 ContextCapture 網路關係圖......................... 50
圖 4-18 網格原點算法 ........................ 51
圖 4-19 網格算法 .............................. 52
圖 4-20 金門土資場點雲圖 ............... 53
圖 4-21 土資場網格圖 ........................ 54
圖 4-22 網格圖詳細情況 ......................... 55
圖 4-23 原始圖片 ................................. 56
圖 4-24 人眼辨識的預期圖片 .................. 56
圖 4-25 分割後圖片 ............................. 57
圖 4-26 灌木叢垃圾分割前後對比圖 (1)...........58
圖 4-27 鐵條分割前後對比圖(2)........................................... 58
圖 4-28 箱子分割前後對比圖(3).......................................... 59
圖 4-29 車輛分割前後比對圖(4)...................................... 59
圖 4-30 挖斗分割前後比對圖(5)..................................... 60
圖 4-31 挖土機分割前後比對圖(6)................................. 60
圖 4-32 土方計算列表 ................ 62
圖 4-33 土方詳細計算 ......................... 62
圖 4-34 金門土方場斜坡的長條型混凝土塊 ......................................... 66
表目錄
表 2-1 地面控制點布置比較 ...................... 9
表 2- 2 各種測量方式比較 ....................... 10
表 2-3 UAV 依搭載設備各有其優缺點 ................................................... 11
表 2-4 RTK 與 PPK 比較 ............................ 13
表 2-5 無人機與 GPS-RTK 成本之比較............................................ 14
表 4- 1 某土方場空拍數據與月報表數據統計結果 ............................................ 64

參考文獻

[1] 中華民國內政部營建署，營建剩餘土石方處理方案，台內營字第1080815785號，民國 108 年 09 月 11 日
[2] 中華民國內政部營建署，公共工程及公有建築工程營建剩餘土石方交換利用作業要點，台內營字第0950801476號，民國 105 年 12 月 07 日R.
[3] Al-Tahir and T. Barran, "EARTHWORK VOLUMETRICS WITH UNMANNED AERIAL VEHICLES: A COMPARATIVE STUDY," in BOOK OF ABSTRACTS, 2020, p. 92.
[4] J. W. Park and D. J. Yeom, "Method for establishing ground control points to realize UAV-based precision digital maps of earthwork sites," Journal of Asian Architecture
and Building Engineering, vol. 21, no. 1, pp. 110-119, 2022.
[5] A. Alshibani, "Automation of measuring actual productivity of earthwork in urban area, a case study from Montreal," Buildings, vol. 8, no. 12, p. 178, 2018.
[6] M. Bügler, A. Borrmann, G. Ogunmakin, P. A. Vela, and J. Teizer, "Fusion of photogrammetry and video analysis for productivity assessment of earthwork processes," Computer‐Aided Civil and Infrastructure Engineering, vol. 32, no. 2, pp. 107-123, 2017.
[7] K. Douterloigne, S. Gautama, and W. Philips, "On the accuracy of 3D landscapes from UAV image data," in 2010 IEEE International Geoscience and Remote Sensing Symposium, 2010: IEEE, pp. 589-592.
[8] Y.-C. Lin et al., "Evaluation of UAV LiDAR for mapping coastal environments," Remote Sensing, vol. 11, no. 24, p. 2893, 2019.
[9] T. Ota, M. Ogawa, N. Mizoue, K. Fukumoto, and S. Yoshida, "Forest structure estimation from a UAV-based photogrammetric point cloud in managed temperate
coniferous forests," Forests, vol. 8, no. 9, p. 343, 2017
[10] L. Wallace, A. Lucieer, C. Watson, and D. Turner, "Development of a UAV-LiDAR system with application to forest inventory," Remote sensing, vol. 4, no. 6, pp. 1519-
1543, 2012.
[11] S. I. Cho, J. H. Lim, S. B. Lim, and H. C. Yun, "A study on dem-based automatic calculation of earthwork volume for BIM application," Journal of the Korean Society
of Surveying, Geodesy, Photogrammetry and Cartography, vol. 38, no. 2, pp. 131-140,
2020.
[12] P. Kavaliauskas, D. Židanavičius, and A. Jurelionis, "Geometric Accuracy of 3D Reality Mesh Utilization for BIM-Based Earthwork Quantity Estimation Workflows," ISPRS International Journal of Geo-Information, vol. 10, no. 6, p. 399, 2021.
[13] 張偉紅、竺會梅、林早紅、李榮浩、龍繼訓，〈無人機在基礎工程土石方量計算中的應用研究〉昆明冶金高等專科學校學報，第36卷，第3期，頁52-56，
2020年。
[14] D. Ronchi, M. Limongiello, and S. Barba, "Correlation among earthwork and cropmark anomalies within archaeological landscape investigation by using LiDAR and multispectral technologies from UAV," Drones, vol. 4, no. 4, p. 72, 2020.
[15] K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961-2969.
[16] T. Cheng, X. Wang, L. Huang, and W. Liu, "Boundary-preserving mask r-cnn," in European conference on computer vision, 2020: Springer, pp. 660-676.
[17] M. Wu et al., "Object detection based on RGC mask R‐CNN," IET Image Processing, vol. 14, no. 8, pp. 1502-1508, 2020.
[18] S. Guan, A. A. Khan, S. Sikdar, and P. V. Chitnis, "Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal," IEEE journal of biomedical and health
informatics, vol. 24, no. 2, pp. 568-576, 2019.
[19] K. Zhao, J. Kang, J. Jung, and G. Sohn, "Building extraction from satellite images using mask R-CNN with building boundary regularization," in Proceedings of the
IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 247-251.
[20] S. Nie, Z. Jiang, H. Zhang, B. Cai, and Y. Yao, "Inshore ship detection based on mask R-CNN," in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018: IEEE, pp. 693-696.
[21] B. Baheti, S. Innani, S. Gajre, and S. Talbar, "Eff-unet: A novel architecture for semantic segmentation in unstructured environment," in Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition Workshops, 2020, pp. 358-359.
[22] J. W. Johnson, "Adapting mask-rcnn for automatic nucleus segmentation," arXiv preprint arXiv:1805.00500, 2018.
[23] Y. Weng, T. Zhou, Y. Li, and X. Qiu, "Nas-unet: Neural architecture search for medical image segmentation," IEEE Access, vol. 7, pp. 44247-44257, 2019.
[24] A. Mathew, P. Amudha, and S. Sivakumari, "Deep learning techniques: an overview," in International conference on advanced machine learning technologies and applications, 2021: Springer, pp. 599-608.
[25] S. Innani, P. Dutande, B. Baheti, S. Talbar, and U. Baid, "Fuse-PN: A Novel Architecture for Anomaly Pattern Segmentation in Aerial Agricultural Images," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition, 2021, pp. 2960-2968.
[26] S. Garg and N. Goyal, "FACE MASK, ROAD TRAFFIC AND LICENSE PLATE DETECTION USING YOLOv3," 2022.
[27] J.-Y. Chiao, K.-Y. Chen, K. Y.-K. Liao, P.-H. Hsieh, G. Zhang, and T.-C. Huang, "Detection and classification the breast tumors using mask R-CNN on sonograms,"
Medicine, vol. 98, no. 19, 2019.
[28] M.-C. Su, J.-H. Chen, A. M. Utami, S.-C. Lin, and H.-H. Wei, "Dove swarm optimization algorithm," IEEE Access, vol. 10, pp. 46690-46696, 2022.
[29] C.-T. Tseng and C.-J. Liao, "A particle swarm optimization algorithm for hybrid flowshop scheduling with multiprocessor tasks," International journal of production
research, vol. 46, no. 17, pp. 4655-4670, 2008.
[30] J. Radosavljević, D. Klimenta, M. Jevtić, and N. Arsić, "Optimal power flow using a hybrid optimization algorithm of particle swarm optimization and gravitational search algorithm," Electric Power Components and Systems, vol. 43, no. 17, pp. 1958-1970, 2015.
[31] Z. Masoomi, M. S. Mesgari, and M. Hamrah, "Allocation of urban land uses by MultiObjective Particle Swarm Optimization algorithm," International Journal of Geographical Information Science, vol. 27, no. 3, pp. 542-566, 2013.
[32] L. Xu, B. Song, and M. Cao, "An improved particle swarm optimization algorithm with adaptive weighted delay velocity," Systems Science & Control Engineering, vol.
9, no. 1, pp. 188-197, 2021.
[33] S. Kheradyar, M. H. Gholizadeh, and F. Lotfi, "Hybrid PCA-ANFIS approach and dove swarm optimization for predicting financial distress," Financial Engineering and
Portfolio Management, vol. 9, no. 37, pp. 133-157, 2018.
[34] N. Zou, Z. Xiang, Y. Chen, S. Chen, and C. Qiao, "Boundary-aware CNN for semantic segmentation," IEEE Access, vol. 7, pp. 114520-114528, 2019.
[35] A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, "Enet: A deep neural network architecture for real-time semantic segmentation," arXiv preprint arXiv:1606.02147,
2016.
[36] Z.-h. Wang, S.-b. Wang, L.-r. Yan, and Y. Yuan, "Road Surface State Recognition Based on Semantic Segmentation," Journal of Highway and Transportation Research and Development (English Edition), vol. 15, no. 2, pp. 88-94, 2021.
[28] M.-C. Su, J.-H. Chen, A. M. Utami, S.-C. Lin, and H.-H. Wei, "Dove swarm
optimization algorithm," IEEE Access, vol. 10, pp. 46690-46696, 2022.
[29] C.-T. Tseng and C.-J. Liao, "A particle swarm optimization algorithm for hybrid flowshop scheduling with multiprocessor tasks," International journal of production
research, vol. 46, no. 17, pp. 4655-4670, 2008.
[30] J. Radosavljević, D. Klimenta, M. Jevtić, and N. Arsić, "Optimal power flow using a
hybrid optimization algorithm of particle swarm optimization and gravitational search
algorithm," Electric Power Components and Systems, vol. 43, no. 17, pp. 1958-1970,
2015.
[31] Z. Masoomi, M. S. Mesgari, and M. Hamrah, "Allocation of urban land uses by MultiObjective Particle Swarm Optimization algorithm," International Journal of
Geographical Information Science, vol. 27, no. 3, pp. 542-566, 2013.
[32] L. Xu, B. Song, and M. Cao, "An improved particle swarm optimization algorithm
with adaptive weighted delay velocity," Systems Science & Control Engineering, vol.
9, no. 1, pp. 188-197, 2021.
[33] S. Kheradyar, M. H. Gholizadeh, and F. Lotfi, "Hybrid PCA-ANFIS approach and
dove swarm optimization for predicting financial distress," Financial Engineering and
Portfolio Management, vol. 9, no. 37, pp. 133-157, 2018.
[34] N. Zou, Z. Xiang, Y. Chen, S. Chen, and C. Qiao, "Boundary-aware CNN for
semantic segmentation," IEEE Access, vol. 7, pp. 114520-114528, 2019.
[35] A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, "Enet: A deep neural network
architecture for real-time semantic segmentation," arXiv preprint arXiv:1606.02147,
2016.
[36] Z.-h. Wang, S.-b. Wang, L.-r. Yan, and Y. Yuan, "Road Surface State Recognition
Based on Semantic Segmentation," Journal of Highway and Transportation Research
and Development (English Edition), vol. 15, no. 2, pp. 88-94, 2021.
[37] B. Baheti, S. Gajre, and S. Talbar, "Semantic scene understanding in unstructured environment with deep convolutional neural network," in TENCON 2019-2019 IEEE
Region 10 Conference (TENCON), 2019: IEEE, pp. 790-795.
[38] M. Kahoush et al., "Analysis of Flight Parameters on UAV Semantic Segmentation Performance for Highway Infrastructure Monitoring," in Computing in Civil
Engineering 2021, pp. 885-893.
[39] Y. Perez-Perez, M. Golparvar-Fard, and K. El-Rayes, "Artificial neural network for semantic segmentation of built environments for automated Scan2BIM," in Computing
in Civil Engineering 2019: Data, Sensing, and Analytics: American Society of Civil Engineers Reston, VA, 2019, pp. 97-104.
[40] Y. Pi, N. D. Nath, and A. H. Behzadan, "Detection and semantic segmentation of disaster damage in UAV footage," Journal of Computing in Civil Engineering, vol. 35,
no. 2, p. 04020063, 2021.
[41] T. Czerniawski and F. Leite, "Semantic segmentation of building point clouds using deep learning: a method for creating training data using BIM to point cloud label
transfer," in Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation: American Society of Civil Engineers Reston, VA, 2019,
pp. 410-416.
[42] D. Bolya, C. Zhou, F. Xiao, and Y. J. Lee, "Yolact: Real-time instance segmentation," in Proceedings of the IEEE/CVF international conference on computer vision, 2019,
pp. 9157-9166.
[43] A. M. Hafiz and G. M. Bhat, "A survey on instance segmentation: state of the art," International journal of multimedia information retrieval, vol. 9, no. 3, pp. 171-189,
2020.
[44] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, "Path aggregation network for instance segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8759-8768.
[45] O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in International Conference on Medical image computing and computer-assisted intervention, 2015: Springer, pp. 234-241.
[46] X. Li, H. Chen, X. Qi, Q. Dou, C.-W. Fu, and P.-A. Heng, "H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes," IEEE transactions on medical imaging, vol. 37, no. 12, pp. 2663-2674, 2018.
[47] H. Huang et al., "Unet 3+: A full-scale connected unet for medical image segmentation," in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020: IEEE, pp. 1055-1059.
[48] Q. Liu et al., "Summary of calculation methods of engineering earthwork," in Journal of Physics: Conference Series, 2021, vol. 1802, no. 3: IOP Publishing, p. 032002.
[49] A. F. Agarap, "Deep learning using rectified linear units (relu)," arXiv preprint arXiv:1803.08375, 2018.
[50] S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a convolutional neural network," in 2017 international conference on engineering and technology
(ICET), 2017: Ieee, pp. 1-6.

指導教授

陳介豪(Jieh-Haur Chen)

審核日期

2023-1-18

推文