计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 418-423.doi: 10.11896/jsjkx.210700210
王建明1, 陈响育1, 杨自忠2, 史晨阳1, 张宇航1, 钱正坤1
WANG Jian-ming1, CHEN Xiang-yu1, YANG Zi-zhong2, SHI Chen-yang1, ZHANG Yu-hang1, QIAN Zheng-kun1
摘要： 深度学习的效果严重依赖于数据的数量与质量,数据量不足将导致模型过拟合。而在实际应用研究中,大量的高质量样本数据往往较难获取,图像数据尤甚。针对上述问题,以ANIMAL-10数据集为基础,设计了一种基于前景目标提取,使用纯色替换原始背景以实现数据增强的方法,并结合传统数据增强方法构造了新数据集,使用AlexNet,Inception-v3,ResNet-50和VGG-16 4个神经网络模型分析不同颜色背景及不同数据增强方法对模型识别精度的影响。实验结果表明:不同颜色背景对模型识别精度无显著影响。以此为基础,采用绿色背景进行后续的数据增强操作,设计了A,B,C,D 4个数据集并对上述4个模型进行了对比实验,实验结果表明模型对识别精度有显著影响而对数据集无显著影响,但对于AlexNet和Inception-v3模型,包含突出前景数据增强数据集的识别精度较原始图像及传统数据增强方式分别提高了3.78%和4.55%,这说明在小数据集下,突出前景的数据增强方法能使模型更容易注意到并学习到图像的关键特征,从而使模型的表现更好,提高模型的识别精度,在实际的工程应用中具有一定的实践价值。
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| [1] ZHU J T,YAO G L,ZHANG G X,et al.Survey of Few Shot Learning of Deep Neural Network[J].CEA,2021,57(7):22-33. [2] YAN L C,YOSHUA B,GEOFFREY H.Deep Learning[J].Nature,2015,521(7553):436-444. [3] JORDAN M I,MITCHELL T M.Machine Learning:Trends,Perspectives,and Prospects[J].Science,2015,349(6245):255-260. [4] ALBAWI S,MOHAMMED T A,AL-ZAWI S.Understandingof aConvolutional Neural Network[C]//International Confe-rence on Engineering and Technology(ICET).IEEE,2017:1-6. [5] NOROUZZADEH M S,NGUYEN A,KOSMALA M,et al.Automatically Identifying,Counting,and Describing Wild Animals in Camera-trap Images with Deep Learning[J].Proceedings of the National Academy of Sciences,2018,115(25):E5716-E5725. [6] DIETTERICH T.Overfitting andUndercomputing in MachineLearning[J].ACM Computing Surveys(CSUR),1995,27(3):326-327. [7] BIEHL M,GHOSH A,HAMMER B.Dynamics and Generalization Ability of LVQ Algorithms[J].Journal of Machine Learning Research,2007,8(8):323-360. [8] KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[J].Communications of the ACM,2017,60(6):84-90. [9] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-scale Image Recognition[C]//International Conference on Learning Representations(ICLR).2015. [10] RUSSO F.An Image Enhancement Technique Combining Shar-pening and Noise Reduction[J].IEEE Transactions on Instrumentation and Measurement,2002,51(4):824-828. [11] RANA P,CHOPRA V.A Study on Image Enhancement Techniques[J].IJARCCE,2015,4(5):609-611. [12] LIU S K,TANG P,JIN W D.Study on Catenary Dropper and Support Detection Based on Intelligent Data Augmentation and Improved YOLOv3[J].Computer Science,2020,47(11A):178-182. [13] BAO Y X,LU T L,DU Y H,et al.Deepfake Videos Detection Method Based on i_ResNet34 Model and Data Augmentation[J].Computer Science,2021,48(7):77-85. [14] ZHONG Z,ZHENG L,KANG G,et al.Random Erasing Data Augmentation[J].Proceedings of the AAAI Conference on Artificial Intelligence,2017,34(7):13001-13008. [15] CHEN P,LIU S,ZHAO H,et al.GridMask Data Augmentation[J].arXiv:2001.04086,2020. [16] PEREZ L,WANG J.The Effectiveness of Data Augmentation in Image Classification using Deep Learning[J].arXiv:1712.04621,2017. [17] GIROSI F,JONES M,POGGIO T.Regularization Theory andNeural Networks Architectures[J].Neural computation,1995,7(2):219-269. [18] SCHWARTZ E,KARLINSKY L,SHTOK J,et al.Delta-en-coder:An Effective Sample Synthesis Method for Few-shot Object Recognition[C]//Proceedings of the 32nd International Conference on Neural Information Processing Systems(NIPS'18).2018:2850-2860. [19] WANG Y Q,YAO Q M,KWOK J T,et al.Generalizing from a Few Examples:A Survey on Few-shot Learning[J].ACM Computing Surveys,2020,53(3):1-34. [20] YUE Z Q,ZHANG H W,SUN Q R,et al.Interventional Few-Shot Learning[J].arXiv:2009.13000,2020. [21] LU J,GONG P H,YE J P,et al.Learning from Very Few Samples:A Survey[J].arXiv:2009.02653,2020. [22] IOFFE S,SZEGEDY C.Batch normalization:AcceleratingDeep Network Training by Reducing Internal Covariate shift[C]//International Conference on Machine Learning(ICML).2015:448-456. [23] RIBEIRO M T,SINGH S,GUESTRIN C.“Why Should ITrust You?” Explaining the Predictions of any Classifier[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:1135-1144. [24] YU Y,WANG L W,ZHANG Y L.Data Enhancement Algorithm Based on the Correlation between Feature Extraction Preference and Background Color[J].Computer Applications,2019,39(11):3172-3177. [25] CORRADO A.Animal Pictures of 10 Different CategoriesTaken from Google Images[EB/OL].(2019-12-13)[2021-07-21].https://www.kaggle.com/alessiocorrado99/animals10/metadata. |
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