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論文名稱(外文):Patch-based Skin Defect Identification Method for Fruit Grading
指導教授(外文):YEH, I-CHENG
外文關鍵詞:Patch-based image classificationObject detection and classificationFeature engineering
  • 被引用被引用:1
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  • 下載下載:58
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Nowadays, there are many different industries and life situations where the introduction of technologies related to artificial intelligence and image recognition can reduce labor and time to improve the speed and reliability of decision making. In this study, we propose a patch-based training approach. We use mangoes as our primary dataset, which can be classified into three classes according to their appearance quality and five types of defects of different degrees. In this thesis, we propose a patch-based classification method to identify and classify the defects of mangoes, which is different from other research results that directly use the whole picture as input to a convolutional neural network. We first use clustering to divide the mango into several superpixels as patches of mango, and we can further process these patches to obtain some statistical features, such as the proportion of defective parts on the surface, representative color, average color, etc. We use a knowledge distillation-based neural network framework for five types of mango defects classification training to obtain the target defect proportion. Finally, we can use these simple statistical feature vectors for image classification. According to the experimental results on the mango dataset, the statistical feature vector proposed in this paper can be used to predict the mango grade with a certain degree of accuracy. If we further combine proposed statistical feature vectors with the traditional color features extracted from the ResNet50 network, the predictive power of the trained model can also be improved. It is expected that this method can be applied not only to mangoes but also to bread, other fruits, or medical images, where the shape and structure of objects are not regular.

Keywords: Patch-based image classification, Object detection and classification, Feature engineering
書名頁 i
論文口試委員審定書 ii
摘要 iii
誌謝 v
目錄 vi
圖目錄 viii
表目錄 x
第一章、緒論 1
1.1 研究背景與動機 1
1.2 章節概要 3
第二章、文獻研究 4
2.1 物件偵測 4
2.2 區塊化影像辨識 5
2.3 知識蒸餾 6
2.4 商品辨識 8
2.5 水果辨識 9
第三章、方法 11
3.1 資料集簡介 11
3.2 研究方法概覽 11
3.3 資料前處理 12
3.3.1 三分類資料集的建立過程 13
(1)取得顯著圖(Saliency Map) 13
(2)物件區塊化 13
(3)圖像修復 14
(4)好壞區塊重新分類 16
3.3.2 六分類資料集建立過程與分類器訓練 16
3.4 芒果等級預測 19
第四章、驗證 22
4.1 實驗規劃 22
4.2 實驗步驟 22
(1)單一簡易統計特徵測試 22
(2)代表色數量交叉測試 23
(3)特徵提取網路測試 24
(4)簡易統計特徵與ResNet50特徵結合測試 25
4.3 實驗結果與討論 27
第五章、結論 28
參考文獻 29

[1] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Vol. 1. IEEE, 2005.
[2] Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
[3] Girshick, Ross., Donahue, Jeff., Darrell, Trevor., & Malik, Jitendra. (2014). 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)..
[4] Ren, Shaoqing., He, Kaiming., Girshick, Ross., & Sun, Jian. (2016). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6), 1137-1149.
[5] Redmon, Joseph., Divvala, Santosh., Girshick, Ross., & Farhadi, Ali. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
[6] Lucey, S., & Chen, Tsuhan. (2004, June). A GMM parts based face representation for improved verification through relevance adaptation. In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. (Vol. 2, pp. II-II). IEEE.
[7] Xu, Chunyan., Wang, Tianjiang., Gao, Junbin., Cao, Shougang., Tao, Wenbing., & Liu, Fang. (2013). An ordered-patch-based image classification approach on the image Grassmannian manifold. IEEE transactions on neural networks and learning systems, 25(4), 728-737.
[8] Yan, Shuicheng., Zhou, Xi., Liu, MingLiu., Hasegawa-Johnson, Mark., & Huang, Thomas. S. (2008, June). Regression from patch-kernel. In 2008 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1-8). IEEE.
[9] Anavi, Yaron., Kogan, Ilya., Gelbart, Elad., Geva, Ofer., & Greenspan, Hayit. (2015, August). A comparative study for chest radiograph image retrieval using binary texture and deep learning classification. In 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 2940-2943). IEEE.
[10] Wang, Guotai., Li, Wenqi., Zuluaga, Maria. A., Pratt, Rosalind., Patel, Premal. A., Aertsen, Michael., Doel, Tom., David, Anna. L., Deprest, Jan., Ourselin, Sébastien. & Vercauteren, Tom. (2018). Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE transactions on medical imaging, 37(7), 1562-1573.
[11] Hinton, Geoffrey., Vinyals, Oriol., & Dean, Jeff. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
[12] Furlanello, Tommaso., Lipton, Zachary. C., Tschannen, Michael., Itti, Laurent., & Anandkumar, Anima. (2018). Born again neural networks. arXiv preprint arXiv:1805.04770.
[13] Nie, Xuecheng., Li, Yuncheng., Luo, Linjie., Zhang, Ning., & Feng, Jiashi. (2019). Dynamic kernel distillation for efficient pose estimation in videos. In Proceedings of the IEEE International Conference on Computer Vision (pp. 6942-6950).
[14] Zhang, Linfeng., Song, Jiebo., Gao, Anni., Chen, Jingwei., Bao, Chenglong., & Ma, Kaisheng. (2019). Be your own teacher: Improve the performance of convolutional neural networks via self distillation. In Proceedings of the IEEE International Conference on Computer Vision (pp. 3713-3722).
[15] Hao, Yu, Yanwei Fu, and Yu-Gang Jiang. "Take Goods from Shelves: A Dataset for Class-Incremental Object Detection." Proceedings of the 2019 on International Conference on Multimedia Retrieval. 2019
[16] Ji, Zhong., Kong, Qiankun., Wang, Haoran., & Pang, Yanwei. (2019, October). Small and Dense Commodity Object Detection with Multi-Scale Receptive Field Attention. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 1349-1357).
[17] Zhao, Linyu., Yao, Jian., Du, Hongyu., Zhao, Jinjie., & Zhang, Ruijie. (2019, September). A Unified Object Detection Framework for Intelligent Retail Container Commodities. In 2019 IEEE International Conference on Image Processing (ICIP) (pp. 3891-3895). IEEE.
[18] Unay, Devrim., & Gosselin, Bernard. (2005, September). Thresholding-based segmentation and apple grading by machine vision. In 2005 13th European Signal Processing Conference (pp. 1-4). IEEE.
[19] Haidar, Abdulhamid., Dong, Haiwei., & Mavridis, Nikolaos. (2012, October). Image-based date fruit classification. In 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems (pp. 357-363). IEEE.
[20] Hossain, M. Shamim., Al-Hammadi, Muneer., & Muhammad, Ghulam. (2018). Automatic fruit classification using deep learning for industrial applications. IEEE transactions on industrial informatics, 15(2), 1027-1034.
[21] Qin, Xuebin., Zhang, Zichen., Huang, Chenyang., Dehghan, Masood., Zaiane, Osmar. R., & Jagersand, Martin. (2020). U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recognition, 106, 107404.
[22] Irving, Benjamin. (2016). maskSLIC: regional superpixel generation with application to local pathology characterisation in medical images. arXiv preprint arXiv:1606.09518.
[23] Achanta, Radhakrishna., Shaji, Appu., Smith, Kevin., Lucchi, Aurélien., Fua, Pascal., & Süsstrunk, Sabine. (2010). Slic superpixels (No. REP_WORK).
[24] Yu, Jiahui., Lin, Zhe., Yang, Jimei., Shen, Xin., Lu, Xin., & Huang, Thomas. S. (2018). Generative image inpainting with contextual attention. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 5505-5514).ISO 690
[25] He, Kaiming., Zhang, Xiangyu., Ren, Shaoqing., & Sun, Jian. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[26] Selvaraju, R. Ramprasaath., Cogswell, Michael., Das, Abhishek., Vedantam, Ramakrishna., Parikh, Devi., & Batra, Dhruv. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618-626)
[27] Chen, Tianqi., & Guestrin, Carlos. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794)

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