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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅楸善 | zh_TW |
| dc.contributor.advisor | Chiou-Shann Fuh | en |
| dc.contributor.author | 凌宇帆 | zh_TW |
| dc.contributor.author | Yu-Fan Ling | en |
| dc.date.accessioned | 2024-08-08T16:26:44Z | - |
| dc.date.available | 2024-08-09 | - |
| dc.date.copyright | 2024-08-08 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-26 | - |
| dc.identifier.citation | D. Barath and J. Matas, "Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 9, pp. 4961-4974, 2021.
H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Speeded-Up Robust Features (SURF)," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008. J. Bergstra, D. Yamins, and D. D. Cox, "Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms," Proceedings of Python in Science Conference, Austin, TX, pp. 13-20, 2013. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," arXiv preprint arXiv:2004.10934, 2020. Z. Cai and N. Vasconcelos, "Cascade R-CNN: Delving into High Quality Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, pp. 6154-6162, doi: 10.1109/CVPR.2018.00644, 2018. J. Canny, "A Computational Approach to Edge Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-698, doi: 10.1109/TPAMI.1986.4767851, 1986. D. DeTone, T. Malisiewicz, and A. Rabinovich, "SuperPoint: Self-Supervised Interest Point Detection and Description," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, Utah, pp. 1-13, 2018. M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler, "D2-Net: A Trainable CNN for Joint Description and Detection of Local Features," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, pp. 1-16, 2019. J. Edstedt, et al., "DKM: Dense Kernelized Feature Matching for Geometry Estimation," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, pp. 1-11, 2023. M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter, "Efficient and Robust Automated Machine Learning," Proceedings of Conference on Neural Information Processing Systems, Workshop on Advances in Neural Information Processing Systems 28, Montreal, Canada, pp. 2962-2970, 2015. M. A. Fischler and R. C. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981. C. Harris and M. Stephens, "A Combined Corner and Edge Detector,” Proceedings of Alvey Vision Conference, Manchester, UK, pp. 147-151, 1988. K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask R-CNN," Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp. 2961-2969, doi: 10.1109/ICCV.2017.322, 2017. Z. Li and N. Snavely, “MegaDepth: Learning Single-View Depth Prediction from Internet Photos,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 2041-2050, doi: 10.1109/CVPR.2018.00218, 2018. T. Y. Lin, M. Maire, S. Belongie, et al., "Microsoft COCO: Common Objects in Context," Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, pp. 740-755, doi: 10.1007/978-3-319-10602-1_48, 2014. T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal Loss for Dense Object Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318-327, doi: 10.1109/TPAMI.2018.2858826, 2020. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, "SSD: Single Shot MultiBox Detector," Proceedings of the European Conference on Computer Vision, Amsterdam, Netherlands, pp. 21-37, doi: 10.1007/978-3-319-46448-0_2, 2016. D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004. D. Marr and E. Hildreth, "Theory of Edge Detection," Proceedings of the Royal Society of London. Series B, Biological Sciences, vol. 207, no. 1167, pp. 187-217, 1980. D. Nister, O. Naroditsky, and J. Bergen, "Visual Odometry," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C., vol. 1, pp. 652-659, 2004. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, pp. 779-788, doi: 10.1109/CVPR.2016.91, 2016. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, pp. 91-99, doi: 10.1109/ICCV.2015.169, 2015. J. Revaud, P. Weinzaepfel, C. De Souza, N. Pion, G. Csurka, Y. Cabon, and M. Humenberger, "R2D2: Repeatable and Reliable Detector and Descriptor," Proceedings of the Neural Information Processing Systems, Vancouver, Canada, pp. 1-12, 2019. E. Rosten and T. Drummond, "Machine Learning for High-Speed Corner Detection," Proceedings of the European Conference on Computer Vision, Graz, Austria, pp. 430-443, 2006. E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An Efficient Alternative to SIFT or SURF," Proceedings of the International Conference on Computer Vision, Barcelona, Spain, pp. 2564-2571, 2011. 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," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, pp. 390-391, doi: 10.1109/CVPRW50498.2020.00203, 2020. C.-Y. Wang, H.-Y. Mark Liao, and I.-H. Yeh, "Designing Network Design Strategies through Gradient Path Analysis," Journal of Information Science and Engineering, vol. 39, no. 4, pp. 975-995, 2023. C.-Y. Wang, I.-H. Yeh, and H.-Y. Mark Liao, "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information," arXiv:2402.13616, 2024. S. Zagoruyko and N. Komodakis, "Learning to Compare Image Patches via Convolutional Neural Networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, pp. 1-9, 2015. H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, "Mixup: Beyond Empirical Risk Minimization," Proceedings of the International Conference on Learning Representations, Vancouver, Canada, pp. 1-13, 2018. H. Zhang and S. Zhang, "Shape-IoU: More Accurate Metric Considering Bounding Box Shape and Scale," arXiv:2312.17663, 2023. B. Zoph and Q. V. Le, "Neural Architecture Search with Reinforcement Learning," Proceedings of the International Conference on Learning Representations, Toulon, France, pp. 1-16, 2017. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93830 | - |
| dc.description.abstract | 本研究著眼於開發一種創新的電腦視覺演算法，名為"凌檢測"，專門用於精確識別和量測電視機背板上螺絲的位置和尺寸。這一演算法的開發是為了實現比人工檢測更高的速度和效率，尤其是在螺絲位置分佈多樣且精確定位至關重要的情境下。
電視機背板上的螺絲分佈依品牌和型號而異，固定方式亦有所不同，包括表面固定的裸露螺絲和位於機殼深處的隱藏螺絲。因此，「凌檢測」演算法被設計為能夠兼容並準確檢測各種電視機背板上所有螺絲的位置。 研究中使用實際檢測到的螺絲準確率作為評估演算法性能的標準。鑒於後續工序需要將這些位置信息提供給機械臂以便進行電視機的拆解和回收，準確性成為至關重要的因素。任何漏檢的螺絲都可能導致拆解工作的失敗。因此，本研究的實驗目標是利用新開發的人工智能演算法，在保證高準確度的前提下，高效完成螺絲位置的檢測工作。 | zh_TW |
| dc.description.abstract | This study focuses on the development of an innovative computer vision algorithm, named "LingInspect," specifically designed to accurately identify and measure the position and size of screws on the back panels of televisions. The development of this algorithm is intended to achieve greater speed and efficiency than manual inspection, particularly in scenarios where diverse screw placements require precise localization.
The distribution of screws on the back panels of televisions varies by brand and model, and the methods of fixation differ as well, including screws that are surface-mounted and exposed, as well as those that are set deeper, below the casing height. Thus, LingInspect algorithm is designed to be compatible with and accurately detect the positions of all screws on various television back panels. In this research, the accuracy of screw detection as measured by the algorithm serves as the standard for assessing its performance. Given that subsequent processes require providing these position data to robotic arms for the disassembly and recycling of televisions, accuracy is of paramount importance. Any missed screws could result in the failure of the disassembly process. Therefore, the experimental objective of this study is to utilize the newly developed artificial intelligence algorithm to efficiently complete screw position detection with high accuracy and speed. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-08T16:26:44Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-08T16:26:44Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Overview 1 1.2 Automatic Optical Inspection 4 1.3 System Architecture 5 1.4 Thesis Organization 7 Chapter 2 Related Works 9 2.1 Feature Detection 9 2.2 Feature Matching 10 2.3 Object Detection 13 2.3.1 Traditional Object Detection 14 2.3.2 AI-Based Object Detection 15 Chapter 3 Background 18 3.1 Graph-Cut RANSAC 18 3.2 DKM 19 3.3 YOLO (You Only Look Once) 21 3.3.1 YOLOv5 24 3.3.2 YOLOv9 28 3.4 Shape-IoU (Intersection over Union) 31 Chapter 4 Methodology 33 4.1 Overview 33 4.2 Feature Matching 34 4.3 Shadow Removing 37 4.4 Object Detection 39 Chapter 5 Experimental Results 43 5.1 Datasets 43 5.2 Implementation Details 44 5.3 Result Analysis 49 Chapter 6 Conclusion and Future Works 53 References 54 | - |
| dc.language.iso | en | - |
| dc.title | 凌檢測：電視機背板螺絲位置檢測 | zh_TW |
| dc.title | LingInspect: Television Back Panel Screw Position Detection | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 方瓊瑤;劉木議 | zh_TW |
| dc.contributor.oralexamcommittee | Qiong-Yao Fang;Mu-Yi Liu | en |
| dc.subject.keyword | 凌檢測,工業產品影像處理,電腦視覺演算法,電視機背板,螺絲位置,人工智慧, | zh_TW |
| dc.subject.keyword | LingInspect,Industrial Product Image Processing,Computer Vision Algorithm,Television Back Panel,Screw Positions,Artificial Intelligence, | en |
| dc.relation.page | 59 | - |
| dc.identifier.doi | 10.6342/NTU202402127 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-07-26 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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