請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88830完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭彥甫 | zh_TW |
| dc.contributor.advisor | Yan-Fu Kuo | en |
| dc.contributor.author | 賴品丞 | zh_TW |
| dc.contributor.author | Pin-Cheng Lai | en |
| dc.date.accessioned | 2023-08-15T17:57:45Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-01 | - |
| dc.identifier.citation | Barni, M., Cappellini, V., & Mecocci, A. (1995). A vision system for automatic inspection of meat quality. Image Analysis and Processing: 8th International Conference, ICIAP'95 San Remo, Italy, September 13–15, 1995 Proceedings 8. (pp. 748-753)
Brigham, E. O. (1988). The fast Fourier transform and its applications. Prentice-Hall, Inc. Chao, K., Chen, Y.-R., Hruschka, W. R., & Gwozdz, F. B. (2002). On-line inspection of poultry carcasses by a dual-camera system. Journal of Food Engineering, 51(3), 185-192. Chao, K., Yang, C.-C., Chen, Y.-R., Kim, M. S., & Chan, D. E. (2007). Fast line-scan imaging system for broiler carcass inspection. Sensing and Instrumentation for Food Quality and Safety, 1, 62-71. Daley, W. D., Carey, R., & Thompson, C. (1995). Real-time color grading and defect detection of food products. Optics in Agriculture, Forestry, and Biological Processing. (pp. 403-411) Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning. (pp. 233-240) De Boer, P.-T., Kroese, D. P., Mannor, S., & Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of operations research, 134, 19-67. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., & Sun, J. (2021). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. (pp. 13733-13742) Finlayson, G. D., Mackiewicz, M., & Hurlbert, A. (2015). Color correction using root-polynomial regression. IEEE transactions on image processing, 24(5), 1460-1470. Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., & He, K. (2017). Accurate, large minibatch sgd: Training imagenet in 1 hour. arXiv preprint arXiv:1706.02677. Joseph Redmon, A. F. (2018). YOLOv3: An Incremental Improvement. https://doi.org/10.48550/arxiv.1804.02767 Khulal, U., Zhao, J., Hu, W., & Chen, Q. (2016). Nondestructive quantifying total volatile basic nitrogen (TVB-N) content in chicken using hyperspectral imaging (HSI) technique combined with different data dimension reduction algorithms. Food Chemistry, 197, 1191-1199. Kim, J., Han, D., Tai, Y.-W., & Kim, J. (2014). Salient region detection via high-dimensional color transform. Proceedings of the IEEE conference on computer vision and pattern recognition. (pp. 883-890) Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2020). Big Transfer (BiT): General Visual Representation Learning. In (pp. 491-507). Springer International Publishing. https://doi.org/10.1007/978-3-030-58558-7_29 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. Li, J., Cheng, J.-h., Shi, J.-y., & Huang, F. (2012). Brief introduction of back propagation (BP) neural network algorithm and its improvement. Advances in Computer Science and Information Engineering: Volume 2. (pp. 553-558) Lin, C.-T., & Lee, C. S. G. (1991). Neural-network-based fuzzy logic control and decision system. IEEE Transactions on computers, 40(12), 1320-1336. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. European conference on computer vision. (pp. 740-755) Liu, S., Tian, G., & Xu, Y. (2019). A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter. Neurocomputing, 338, 191-206. Man, K.-F., Tang, K.-S., & Kwong, S. (1996). Genetic algorithms: concepts and applications [in engineering design]. IEEE transactions on Industrial Electronics, 43(5), 519-534. Park, B., Chen, Y., & Nguyen, M. (1998). Multi-spectral image analysis using neural network algorithm for inspection of poultry carcasses. Journal of Agricultural Engineering Research, 69(4), 351-363. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., & Lerer, A. (2017). Automatic differentiation in pytorch. Putra, G. B., & Prakasa, E. (2020). Classification of Chicken Meat Freshness using Convolutional Neural Network Algorithms. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT). (pp. 1-6) Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28. Ren, X., Wang, Y., Huang, Y., Mustafa, M., Sun, D., Xue, F., Chen, D., Xu, L., & Wu, F. (2023). A CNN-Based E-Nose Using Time Series Features for Food Freshness Classification. IEEE Sensors Journal, 23(6), 6027-6038. Ruder, S. (2016). An overview of gradient descent optimization algorithms. https://doi.org/10.48550/arxiv.1609.04747 Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision. (pp. 618-626) Shu Liu, L. Q., Haifang Qin, Jianping Shi, Jiaya Jia. (2018). Path Aggregation Network for Instance Segmentation. https://doi.org/10.48550/arxiv.1803.01534 Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI conference on artificial intelligence. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. (pp. 1-9) Törn, A., & Zilinskas, A. (1989). Global optimization (Vol. 350). Springer. Taheri‐Garavand, A., Fatahi, S., Shahbazi, F., & de la Guardia, M. (2019). A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique. Journal of Food Process Engineering, 42(4), e13039. Tzutalin, D. (2015). LabelImg. GitHub repository, 6. Vincent, L. (1993). Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE transactions on image processing, 2(2), 176-201. Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. https://doi.org/10.48550/arxiv.2207.02696 Wang, C. Y., Liao, H. Y., & Yeh, I. H. (2022). Designing Network Design Strategies Through Gradient Path Analysis. https://doi.org/10.48550/arxiv.2211.04800 Ye, C.-w., Yousaf, K., Qi, C., Liu, C., & Chen, K.-j. (2020). Broiler stunned state detection based on an improved fast region-based convolutional neural network algorithm. Poultry Science, 99(1), 637-646. Yoon, S. C., Park, B., Lawrence, K. C., Windham, W. R., & Heitschmidt, G. W. (2011). Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta. Computers and Electronics in Agriculture, 79(2), 159-168. Zou, M., & Kageyama, Y. (2022). A Deep Color Correction Model for Chicken Meat Images. 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech). (pp. 594-595) | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88830 | - |
| dc.description.abstract | 雞肉被廣泛認為是最常見的肉類之一，同時也在經濟上扮演著重要角色。為了提供消費者高品質的雞肉，必須在雞隻屠宰過程中挑選出有瑕疵之屠體。台灣有色肉雞屬於國內特有品種，相較於白肉雞，有色肉雞品種多樣性高，在外觀顏色與尺寸上都有相當大的差異，由於這些差異，目前市面上用於白肉雞之自動屠體瑕疵檢測系統並不適用於台灣有色肉雞。現行方法採用人力方式來挑選台灣有色肉雞屠體中的瑕疵部分，然而這種方法耗時且效率低下。因此，本研究旨在使用卷積神經網絡來實現對瑕疵屠體的自動辨識。為此，我們開發了一套影像模組，該模組安裝在屠宰產線上，用於收集屠體影像；並提出了兩階段的檢測方法來自動檢測有瑕疵之屠體。在第一階段，使用身體部位分割模型（BSM）來偵測雞隻屠體影像中的翅膀、胸部、背部和腳等部位。隨後，在第二階段，使用了瑕疵分類模型（DCM），分類出具有瑕疵之身體部位。在實驗結果中，BSM在辨識雞隻屠體部位達到了98.9％的平均精確度，整體F1-score為97.4％；DCM在分類瑕疵之身體部位達到了96.9％的準確率。本研究所提出之方法期望能夠提高台灣有色肉雞屠宰產線在瑕疵檢測上之效率與準確性。 | zh_TW |
| dc.description.abstract | Chicken is among the most commonly consumed meats worldwide. To provide high-quality chicken meat to customers, it is necessary to identify carcasses with defections and excluded the defected carcasses in the slaughter process. Taiwan native chickens (TNCs) are popular chicken varieties in the domestic market. Different from the broiler, TNCs include breeds with various sizes and external appearances. These varieties are usually processed in the same slaughter line. Due to these differences, TNC carcasses with defects are usually picked out in the slaughter line manually. However, manual methods are time-consuming and inefficient. Moreover, commercially available automatic carcass defect detection systems for broilers are suboptimal for TNCs due to the high variation. Thus, this study developed a machine vision system to automatically detect the TNC carcasses with defects in slaughter lines. The system comprised an image acquisition module, body parts segmentation model (BSM), and a defect classification model (DCM). The image acquisition module was water-tight and was designed to acquire complete two-sided images of TNC carcasses in a slaughter process line. The BSM was used to detect four body parts of chicken carcasses, namely the wings, breast, back, and legs. Subsequently, the DCM was used to determine whether the body parts were defected. The trained BSM achieved a mean average precision of 98.9%, and an overall F1-score of 97.4% on identifying the carcass body parts. The trained DCM achieved an accuracy of 96.9% on classifying defected body parts. The developed system is expected to enhance the efficiency and effectiveness in detecting defect parts of TNCs. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:57:45Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:57:45Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
ACKNOWLEDGEMENTS ii 摘要 iii ABSTRACT iv TABLE OF CONTENTS v LIST OF FIGURES vii LIST OF TABLES x CHAPTER 1. INTRODUCTION 1 1.1 Background 1 1.2 Objectives 2 1.3 Organization 2 CHAPTER 2. LITERATURE REVIEW 3 2.1 Image-processing-based and machine learning-based approaches 3 2.2 Deep learning approaches for defect classification 4 CHAPTER 3. MATERIALS AND METHODS 6 3.1 Experimental site 6 3.2 Carcass of native Taiwanese chickens and system pipeline 7 3.3 Image acquisition of chicken carcasses 8 3.4 Image collection and preparation 9 3.5 Chicken carcass body parts segmentation 11 3.6 Chicken body parts defect classification 13 3.7 Visual explanation of the CNNs model 14 CHAPTER 4. RESULTS AND DISCUSSION 15 4.1 Training loss and mAP of BSM 15 4.2 Performance of BSM 15 4.3 Challenging scenarios in body parts segmentation 16 4.4 Training loss and accuracy of DCM 18 4.5 Performance of DCM 19 4.6 Challenge scenario in DCM 20 CHAPTER 5. CONCLUSIONS 23 REFERENCES 24 | - |
| dc.language.iso | en | - |
| dc.title | 利用卷積神經網路偵測有色肉雞屠體之瑕疵 | zh_TW |
| dc.title | Detecting Carcass Defects of Native Chickens Using Convolutional Neural Networks | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 謝明昆;陳永耀;陳志維 | zh_TW |
| dc.contributor.oralexamcommittee | Ming-Kun Hsieh;Yung-Yao Chen;Zhi-Wei Chen | en |
| dc.subject.keyword | 卷積神經網路,深度學習,雞隻屠體,瑕疵分類,台灣有色肉雞, | zh_TW |
| dc.subject.keyword | Convolutional neural networks,Deep learning,Chicken carcass,Defects classification,Taiwan native chicken, | en |
| dc.relation.page | 28 | - |
| dc.identifier.doi | 10.6342/NTU202302366 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-04 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物機電工程學系 | - |
| 顯示於系所單位： | 生物機電工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-2.pdf | 10.87 MB | Adobe PDF | 檢視/開啟 |
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。