請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85093完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益(Ray-I Chang) | |
| dc.contributor.author | Ke-Kai Huang | en |
| dc.contributor.author | 黃科凱 | zh_TW |
| dc.date.accessioned | 2023-03-19T22:43:12Z | - |
| dc.date.copyright | 2022-08-18 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-08-11 | |
| dc.identifier.citation | [1] J. S. Lauridsen, J. A. Graasmé, M. Pedersen, D. G. Jensen, S. H. Andersen, and T. B. Moeslund, 'Reading circular analogue gauges using digital image processing,' in 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Visigrapp 2019), 2019: SCITEPRESS Digital Library, pp. 373-382. [2] T. Selvathai, S. Ramesh, and K. Radhakrishnan, 'Automatic interpretation of analog dials in driver's instrumentation panel,' in 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), 2017: IEEE, pp. 411-415. [3] M. Yifan, J. Qi, W. Junjie, and T. Guohui, 'An automatic reading method of pointer instruments,' in 2017 Chinese Automation Congress (CAC), 2017: IEEE, pp. 1448-1453. [4] Y. Ma, Q. J. M. S. Jiang, and Technology, 'A robust and high-precision automatic reading algorithm of pointer meters based on machine vision,' Measurement Science and Technology, vol. 30, no. 1, p. 015401, 2018. [5] J. Chi, L. Liu, J. Liu, Z. Jiang, and G. J. M. P. i. E. Zhang, 'Machine vision based automatic detection method of indicating values of a pointer gauge,' Mathematical Problems in Engineering vol. 2015, p. 19, 2015. [6] Z. Li, Y. Zhou, Q. Sheng, K. Chen, and J. J. S. Huang, 'A High-Robust Automatic Reading Algorithm of Pointer Meters Based on Text Detection,' Sensors, vol. 20, no. 20, p. 5946, 2020. [7] Q. Sheng, L. Zhu, Z. Shao, and J. J. C. J. S. I. Jiang, 'Automatic reading method of pointer meter based on double Hough space voting,' Chinese Journal of Scientific Instrument, vol. 40, no. Issue 5, pp. 230-239, 2019. [8] C. Zheng, S. Wang, Y. Zhang, P. Zhang, and Y. Zhao, 'A robust and automatic recognition system of analog instruments in power system by using computer vision,' Measurement, vol. 92, pp. 413-420, 2016. [9] P. He, L. Zuo, C. Zhang, and Z. Zhang, 'A value recognition algorithm for pointer meter based on improved Mask-RCNN,' in 2019 9th International Conference on Information Science and Technology (ICIST), 2019: IEEE, pp. 108-113. [10] B. Howells, J. Charles, and R. Cipolla, 'Real-time analogue gauge transcription on mobile phone,' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 2369-2377. [11] M. Yi, Z. Yang, F. Guo, and J. Liu, 'A clustering-based algorithm for automatic detection of automobile dashboard,' in IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society, 2017: IEEE, pp. 3259-3264. [12] 'keras-OCR.' https://keras-ocr.readthedocs.io/en/latest/ (accessed. [13] D. J. Jobson, Z.-u. Rahman, and G. A. Woodell, 'A multiscale retinex for bridging the gap between color images and the human observation of scenes,' IEEE Transactions on Image processing, vol. 6, no. 7, pp. 965-976, 1997. [14] D. Bradley and G. Roth, 'Adaptive thresholding using the integral image,' Journal of graphics tools, vol. 12, no. 2, pp. 13-21, 2007. [15] L. Lam, S.-W. Lee, and C. Y. Suen, 'Thinning methodologies-a comprehensive survey,' IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. 09, pp. 869-885, 1992. [16] L. Vincent and P. Soille, 'Watersheds in digital spaces: an efficient algorithm based on immersion simulations,' IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 13, no. 06, pp. 583-598, 1991. [17] N. Otsu, 'A threshold selection method from gray-level histograms,' IEEE transactions on systems, man, and cybernetics, vol. 9, no. 1, pp. 62-66, 1979. [18] J. MacQueen, 'Some methods for classification and analysis of multivariate observations,' in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, vol. 1, no. 14: Oakland, CA, USA, pp. 281-297. [19] X. Liu, D. Liang, S. Yan, D. Chen, Y. Qiao, and J. Yan, 'Fots: Fast oriented text spotting with a unified network,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 5676-5685. [20] J. Huang, J. Wang, Y. Tan, D. Wu, and Y. Cao, 'An automatic analog instrument reading system using computer vision and inspection robot,' IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6322-6335, 2020. [21] H. Bay, T. Tuytelaars, and L. V. Gool, 'Surf: Speeded up robust features,' in European conference on computer vision, 2006: Springer, pp. 404-417. [22] W. Cai, B. Ma, L. Zhang, and Y. Han, 'A pointer meter recognition method based on virtual sample generation technology,' Measurement, vol. 163, p. 107962, 2020. [23] X. Meng et al., 'Research on reading recognition method of pointer meters based on deep learning combined with rotating virtual pointer,' in 2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), 2020: IEEE, pp. 115-118. [24] A. Newell, K. Yang, and J. Deng, 'Stacked hourglass networks for human pose estimation,' in European conference on computer vision, 2016: Springer, pp. 483-499. [25] K. Duan, S. Bai, L. Xie, H. Qi, Q. Huang, and Q. Tian, 'Centernet: Keypoint triplets for object detection,' in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 6569-6578. [26] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, 'Yolov4: Optimal speed and accuracy of object detection,' arXiv preprint arXiv:2004.10934, 2020. [27] C.-Y. Wang, H.-Y. M. Liao, Y.-H. Wu, P.-Y. Chen, J.-W. Hsieh, and I.-H. Yeh, 'CSPNet: A new backbone that can enhance learning capability of CNN,' in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 2020, pp. 390-391. [28] K. He, X. Zhang, S. Ren, and J. Sun, 'Spatial pyramid pooling in deep convolutional networks for visual recognition,' IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 9, pp. 1904-1916, 2015. [29] 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. [30] J. Redmon and A. Farhadi, 'Yolov3: An incremental improvement,' arXiv preprint arXiv:1804.02767, 2018. [31] S. Suzuki, 'Topological structural analysis of digitized binary images by border following,' Computer vision, graphics, and image processing, vol. 30, no. 1, pp. 32-46, 1985. [32] 'Pressure Gauge Reader Data.' https://www.kaggle.com/datasets/juliusgrassme/pressure-gauge-reader-data (accessed. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85093 | - |
| dc.description.abstract | 本碩士論文提出一個自動化讀取類比儀表板數值的演算法,類比儀表板種類主要分為圓形及扇形兩種,在傳統上,圓形儀表板有讀取圓形儀表板數值的方法,讀取扇形儀表板數值也有獨立的方法,本論文提出一個可以同時讀取圓形與扇形儀表板的演算法。所提演算法利用每個儀表板都擁有刻度線這個特性,使用主成分分析(principal components analysis, PCA)技術找儀表板中心位置,再擷取出主刻度線,計算每條主刻度線在儀表板中的角度,然後使用光學字元辨識(optical character recognition, OCR)相關技術來偵測儀表板中的數值,因為使用的OCR並非針對儀表板數值設計,所提演算法需處理當儀表板數值負數與含有小數點等問題,再將主刻度線與所找到的數值做配對,接著找出指針的端點並計算其相對於儀表板中心的角度,最後使用這些角度做線性內插,算出儀表板的數值。實驗結果顯示所提演算法可以正確的讀取出儀表板的數值,其平均誤差小於0.5%。 | zh_TW |
| dc.description.abstract | Abstract The goal of this thesis is to present an algorithm to automatically read the values of the analog circular and arc gauges. Traditionally, the circular gauges have specific methods for gauge reading. There are also independent schemes for reading the arc gauges. This thesis proposes a general algorithm for reading of circular and arc gauges. Since both the circular and arc gauges have scale marks, the proposed algorithm first utilizes principal components analysis (PCA) technology to find the first eigenvector of each scale mark. The intersection of these eigenvectors is extracted as the center of the gauge. Main scale marks are obtained and dial values are extracted by optical character recognition (OCR) technology. Since the used OCR is not designed by gauges, the proposed algorithm has to process the gauge with negative values and floating point. Main scale marks and gauge values are bound together from the viewpoint of angles. The peak of the pointer and the corresponding angles to gauge center are further extracted. Finally, the gauge value is obtained by angle interpolation. Experimental results demonstrate that the proposed algorithm can successfully read the values of a variety of circular and arc gauges. The corresponding errors are limited in 0.5%. | en |
| dc.description.provenance | Made available in DSpace on 2023-03-19T22:43:12Z (GMT). No. of bitstreams: 1 U0001-1008202222484600.pdf: 5330369 bytes, checksum: 244f7e92ab21f7aa5601da97af259928 (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書 ii 致謝 iii 中文摘要 iv Abstract v 目錄 vi 圖目錄 viii 表目錄 x 第一章 前言 1 第二章 文獻探討 7 第三章 SGR(Scale-mark-based Gauge Reading)演算法 13 3.1. 儀表板之偵測 13 3.2. 前處理 15 3.3. 儀表板中心偵測 16 3.3.1. CCs之偵測 16 3.3.2. 刻度線之偵測 16 3.3.3. 儀表板中心座標之偵測 17 3.4. 主刻度線與刻度值之配對機制 20 3.4.1. 主刻度線之偵測 21 3.4.2. 刻度值之偵測與推論 25 3.4.3. 主刻度線與刻度值之配對 28 3.5. 以角度為主的儀表板數值讀取機制 30 3.6. 所提方法之系統環境設定 32 3.7. 環境異常之分析與處理 33 第四章 實驗結果及效能分析 38 4.1. 所提SGR儀表板中心偵測演算法之效能 38 4.2. 所提SGR儀表板數值讀取演算法之效能 39 4.3. 環境異常影響之效能分析 41 4.3.1. 透視變形對所提儀表板數值讀取演算法的影響 41 4.3.2. 不同光源對所提儀表板數值讀取演算法的影響 43 第五章 結論與未來研究方向 50 參考文獻 51 | |
| dc.language.iso | zh-TW | |
| dc.subject | 儀表板數值讀取 | zh_TW |
| dc.subject | 主成分分析 | zh_TW |
| dc.subject | 光學字元辨識 | zh_TW |
| dc.subject | 儀表板中心偵測 | zh_TW |
| dc.subject | Gauge value reading | en |
| dc.subject | Principal components analysis (PCA) | en |
| dc.subject | Optical character recognition (OCR) | en |
| dc.subject | Gauge center detection | en |
| dc.title | 以刻度線為基礎之圓形及扇形儀表板數值讀取演算法的研究 | zh_TW |
| dc.title | A study of scale-mark-based gauge reading algorithm for circular and arc gauges | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 110-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張恆華(Herng-Hua Chang),王家輝(Chia-Hui Wang) | |
| dc.subject.keyword | 主成分分析,光學字元辨識,儀表板中心偵測,儀表板數值讀取, | zh_TW |
| dc.subject.keyword | Principal components analysis (PCA),Optical character recognition (OCR),Gauge center detection,Gauge value reading, | en |
| dc.relation.page | 54 | |
| dc.identifier.doi | 10.6342/NTU202202274 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2022-08-12 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2022-08-18 | - |
| 顯示於系所單位: | 工程科學及海洋工程學系 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1008202222484600.pdf 授權僅限NTU校內IP使用(校園外請利用VPN校外連線服務) | 5.21 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
