融合狗臉和體型雙模態特徵的比特犬辨識

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：11

、訪客IP：3.16.137.217

姓名

王晨瑋(Chen-Wei Wang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

融合狗臉和體型雙模態特徵的比特犬辨識
(Fusion of Dog Face and Body Shape Bimodal Features for Pit Bull-Typed Dogs Recognition)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2028-7-14以後開放)

摘要(中)

全球各地比特犬襲擊人畜新聞頻繁出現，各國政府開始訂定比特犬禁養或列管政策，比特犬自動辨識就成了行政上必要的工具。本研究提出了一種基於體型/狗臉雙模態特徵辨識方法來識別比特犬種，我們使用Segment Anything Model(SAM)網路模型來獲取比特犬體型外觀和狗臉範圍，再利用ResNet進行遷移式學習，同時學習比特犬種的臉部外觀特徵與體型外觀特徵並進行融合決策辨識。在實驗方面，我們根據不同應用場景設計三個實驗來驗證雙模態辨識方法的可靠性，分別是比特犬種與非比特犬種的辨識實驗、管制犬與非管制相似混血比特犬的辨識實驗以及管制犬與非管制犬的辨識實驗，實驗結果顯示在比特犬種與非比特犬種之間的鑑別，雙模態辨識方式則可達到97.4%的辨識準確率；而在管制比特犬與非管制之比特混血犬種之間的辨識，雙模態辨識方式能夠達到90.2%辨識準確率；最後在管制犬與非管制犬之間的辨識，我們的方法能夠達到87.84%的辨識準確率。

摘要(英)

Frequent reports of human and livestock attacked by pit bulls have led governments worldwide to establish policies prohibiting or regulating the ownership of pit bulls. Automatic recognition of pit bulls has become a necessary administrative tool. In this paper, we propose a dual-modal feature recognition method based on body size and dog face to identify pit bull breeds. We use the Segment Anything Model (SAM) network model to extract the appearance and facial regions of pit bulls, and then employ ResNet for transfer learning. The method simultaneously learns facial and body appearance features of pit bull breeds and performs fused decision recognition. In terms of experiments, we design three experiments to verify the reliability of the dual-modal recognition method in different application scenarios. These experiments include the recognition of pit bull breeds versus non-pit bull breeds, the recognition of regulated pit bulls versus non-regulated pit bull mixes, and the recognition of regulated dogs versus non-regulated dogs. The experimental results demonstrate that the bimodal recognition approach achieves a recognition accuracy of 97.4% in discriminating between pit bull breeds and non-pit bull breeds. For the recognition of regulated pit bulls and non-regulated pit bull mixes, the bimodal recognition approach achieves a recognition accuracy of 90.2%. Lastly, in the recognition of regulated dogs and non-regulated dogs, our method achieves a recognition accuracy of 87.84%.

關鍵字(中)

★ 深度學習
★ 生物辨識
★ 遷移式學習

關鍵字(英)

論文目次

目錄
摘要 I
Abstract II
謝誌 III
目錄 V
圖目錄 VII
表目錄 XI
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 4
1.3 論文架構 5
第二章、文獻回顧 6
2.1 YOLO物件偵測神經網路 6
2.1.1 YOLOv3簡介 7
2.1.2 YOLOv4改進 11
2.2 Segment Anything Model簡介 14
2.3 ResNet殘差神經網路簡介 16
第三章、比特犬辨識系統設計 19
3.1 系統設計方法論 19
3.1.1 系統架構設計 20
3.1.2 GRAFCET離散事件建模 22
3.2 比特犬辨識系統架構 25
3.3 比特犬辨識系統GRAFCET 28
3.3.1 SAM影像分割模組GRAFCET 29
3.3.2 ResNet辨識模組GRAFCET 30
3.3.3 融合決策模組GRAFCET 32
第四章、實驗結果 33
4.1 實驗軟硬體開發環境 33
4.2 辨識實驗資料集 34
4.3 影像分割實驗 37
4.3.1 YOLO物件偵測影像分割 37
4.3.2 SAM實例影像分割 39
4.4 比特犬種與非比特犬種辨識實驗 40
4.4.1 YOLO&ResNet的雙模態辨識 40
4.4.2 SAM&ResNet的雙模態辨識與比較 44
4.5 管制比特犬與非管制相似混血犬辨識實驗 52
4.5.1 YOLO&ResNet的雙模態辨識 52
4.5.2 SAM&ResNet的雙模態辨識與比較 56
4.6 管制犬與非管制犬辨識實驗 62
4.6.1 YOLO&ResNet的雙模態辨識 62
4.6.2 SAM&ResNet的雙模態辨識與比較 66
第五章、結論與未來展望 73
5.1 結論 73
5.2 未來展望 74
參考文獻 75

參考文獻

參考文獻
[1] Dogsbite.org. 2019 Dog Bite Fatality Statistics [Online]. Available: https://www.dogsbite.org/dog-bite-statistics-fatalities-2019.php
[2] K. R. Olson, J. Levy, B. Norby, M. Crandall, J. Broadhurst, S. Jacks, R. Barton, and M. Zimmerman, "Inconsistent identification of pit bull-type dogs by shelter staff," The Veterinary Journal, vol. 206, no. 2, pp. 197-202, 2015.
[3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
[4] Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, "Object detection with deep learning: A review," IEEE transactions on neural networks and learning systems, vol. 30, no. 11, pp. 3212-3232, 2019.
[5] R. Raina, A. Madhavan, and A. Y. Ng, "Large-scale deep unsupervised learning using graphics processors," in Proceedings of the 26th annual international conference on machine learning, pp. 873-880, 2009.
[6] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015.
[7] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
[8] J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132-7141, 2018.
[9] Z. Alyafeai and L. Ghouti, "A fully-automated deep learning pipeline for cervical cancer classification," Expert Systems with Applications, vol. 141, p. 112951, 2020.
[10] M. Ahmad, T. M. Ghazal, and N. Aziz, "A survey on animal identification techniques past and present," International Journal of Computational and Innovative Sciences, vol. 1, no. 2, pp. 1-7, 2022.
[11] D. Schofield, A. Nagrani, A. Zisserman, M. Hayashi, T. Matsuzawa, D. Biro, and S. Carvalho, "Chimpanzee face recognition from videos in the wild using deep learning," Science advances, vol. 5, no. 9, p. eaaw0736, 2019.
[12] H. B. Bae, D. Pak, and S. Lee, "Dog nose-print identification using deep neural networks," IEEE Access, vol. 9, pp. 49141-49153, 2021.
[13] A. Kumar and A. Kumar, "Dog breed classifier for facial recognition using convolutional neural networks," in 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), pp. 508-513, 2020.
[14] P. Prasong and K. Chamnongthai, "Face-recognition-based dog-breed classification using size and position of each local part, and pca," in 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 1-5, 2012.
[15] J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
[16] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, "Yolov4: Optimal speed and accuracy of object detection," arXiv preprint arXiv:2004.10934, 2020.
[17] A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, and W.-Y. Lo, "Segment anything," arXiv preprint arXiv:2304.02643, 2023.
[18] R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587, 2014.
[19] R. Girshick, "Fast r-cnn," in Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
[20] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, vol. 28, 2015.
[21] K. He, G. Gkioxari, P. Dollár, and R. Girshick, "Mask r-cnn," in Proceedings of the IEEE international conference on computer vision, pp. 2961-2969, 2017.
[22] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, "SSD: Single shot multibox detector," in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21-37, 2016.
[23] S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, "Single-shot refinement neural network for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4203-4212, 2018.
[24] T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature pyramid networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117-2125, 2017.
[25] 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, pp. 390-391, 2020.
[26] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
[27] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, "Image segmentation using deep learning: A survey," IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 7, pp. 3523-3542, 2021.
[28] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, "Masked autoencoders are scalable vision learners," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000-16009, 2022.
[29] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, and S. Gelly, "An image is worth 16x16 words: Transformers for image recognition at scale," arXiv preprint arXiv:2010.11929, 2020.
[30] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, and J. Clark, "Learning transferable visual models from natural language supervision," in International conference on machine learning, pp. 8748-8763, 2021.
[31] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, "End-to-end object detection with transformers," in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16, pp. 213-229, 2020.
[32] X. Glorot and Y. Bengio, "Understanding the difficulty of training deep feedforward neural networks," in Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 249-256, 2010.
[33] Y. Bengio, P. Simard, and P. Frasconi, "Learning long-term dependencies with gradient descent is difficult," IEEE transactions on neural networks, vol. 5, no. 2, pp. 157-166, 1994.
[34] S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in International conference on machine learning, pp. 448-456, 2015.
[35] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, "High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017.
[36] Y.-T. Chung, " Residual neural network based recognition in pit bull type dogs," National Central University, 2021.
[37] J. N. Mandrekar, "Receiver operating characteristic curve in diagnostic test assessment," Journal of Thoracic Oncology, vol. 5, no. 9, pp. 1315-1316, 2010.

指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2023-7-25

推文