[1] 李敏, 赵影, 张丽平, 等. 涤纶针织物数码印花清晰度的影响因素[J]. 纺织学报, 2018, 39(5): 62-66.

    Li M, Zhao Y, Zhang L P, et al. Factors influencing printing accuracy of digital printing for knitted polyester fabric[J]. Journal of Textile Research, 2018, 39(5): 62-66.

[2] 邓新. 基于嵌入式GPU的数码印花缺陷检测算法研发[D]. 杭州: 浙江大学, 2018: 14- 16.

    DengX. Development of defects detection algorithms for digital textile printing based on embedded GPU[D]. Hangzhou: Zhejiang University, 2018: 14- 16.

[3] 任欢欢, 景军锋, 张缓缓, 等. 应用GIS和FTDT的织物错花缺陷检测研究[J]. 激光与光电子学进展, 2019, 56(13): 131005.

    Ren H H, Jing J F, Zhang H H, et al. Cross-printing defect detection of printed fabric using GIS and FTDT[J]. Laser & Optoelectronics Progress, 2019, 56(13): 131005.

[4] 王晨煜, 景军锋, 张缓缓, 等. 基于滚动引导滤波与FT算法的色织物疵点检测[J]. 棉纺织技术, 2019, 47(6): 28-33.

    Wang C Y, Jing J F, Zhang H H, et al. Defect detection of yarn-dyed fabric based on rolling guide filtering and FT sailiency[J]. Cotton Textile Technology, 2019, 47(6): 28-33.

[5] 卢荣胜, 吴昂, 张腾达, 视觉, 等. 检测技术及其在缺陷检测中的应用综述[J]. 光学学报, 2018, 38(8): 0815002.

    Lu R S, Wu A, Zhang T D, et al. Review on automated optical (visual) inspection and its applications in defect detection[J]. Acta Optica Sinica, 2018, 38(8): 0815002.

[6] Jeyaraj P R. Nadar E R S. Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm[J]. International Journal of Clothing Science and Technology, 2019, 31(4): 510-521.

[7] 卓东, 景军锋, 张缓缓, 等. 基于卷积神经网络的短切毡缺陷分类[J]. 激光与光电子学进展, 2019, 56(10): 101009.

    Zhuo D, Jing J F, Zhang H H, et al. Classification of chopped strand mat defects based on convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101009.

[8] Mottalib MM, RokonuzzamanM, Habib MT, et al.Fabric defect classification with geometric features using Bayesian classifier[C]∥2015 International Conference on Advances in Electrical Engineering (ICAEE), December 17-19, 2015, Dhaka, Bangladesh.New York: IEEE Press, 2015: 137- 140.

[9] 赵鹏, 唐英杰, 杨牧, 等. 卷积神经网络在无纺布缺陷分类检测中的应用[J]. 包装工程, 2020, 41(5): 192-196.

    Zhao P, Tang Y J, Yang M, et al. Application of convolutional neural network in classification and detection of non-woven fabric defects[J]. Packaging Engineering, 2020, 41(5): 192-196.

[10] 景军锋, 范晓婷, 李鹏飞, 等. 应用深度卷积神经网络的色织物缺陷检测[J]. 纺织学报, 2017, 38(2): 68-74.

    Jing J F, Fan X T, Li P F, et al. Yarn-dyed fabric defect detection based on deep-convolutional neural network[J]. Journal of Textile Research, 2017, 38(2): 68-74.

[11] 王巧, 宋柳叶, 王伊千, 等. 数码印花图案及其在丝绸服装设计中的应用路径[J]. 纺织导报, 2019( 3): 82- 85.

    WangQ, Song LY, Wang YQ, et al. Design and application of digital printing in silk garments[J]. China Textile Leader, 2019( 3): 82- 85.

[12] 李勇, 林小竹, 蒋梦莹. 基于跨连接LeNet-5网络的面部表情识别[J]. 自动化学报, 2018, 44(1): 176-182.

    Li Y, Lin X Z, Jiang M Y. Facial expression recognition with cross-connect LeNet-5 network[J]. Acta Automatica Sinica, 2018, 44(1): 176-182.

[13] Wang R, Xu J W, Han T X. Object instance detection with pruned AlexNet and extended training data[J]. Signal Processing: Image Communication, 2019, 70: 145-156.

[14] Swasono DI, TjandrasaH, FathicahC. Classification of tobacco leaf pests using VGG16 transfer learning[C]∥2019 12th International Conference on Information & Communication Technology and System (ICTS), July 18-18, 2019, Surabaya, Indonesia.New York: IEEE Press, 2019: 176- 181.

[15] 张宏伟, 张凌婕, 李鹏飞, 等. 基于GoogLeNet的色织物花型分类[J]. 纺织科技进展, 2017( 7): 33- 35, 52.

    Zhang HW, Zhang LJ, Li PF, et al. Pattern classification of yarn-dyed fabrics based on GoogLeNet[J]. Progress in Textile Science & Technology, 2017( 7): 33- 35, 52.

[16] 马俊成, 赵红东, 杨东旭, 等. 飞机目标分类的深度卷积神经网络设计优化[J]. 激光与光电子学进展, 2019, 56(23): 231006.

    Ma J C, Zhao H D, Yang D X, et al. Design and optimization of deep convolutional neural network for aircraft target classification[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231006.

[17] 谭光鸿, 侯进, 韩雁鹏, 等. 基于卷积神经网络的低参数量实时图像分割算法[J]. 激光与光电子学进展, 2019, 56(9): 091003.

    Tan G H, Hou J, Han Y P, et al. Low-parameter real-time image segmentation algorithm based on convolutional neural network[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091003.

[18] IoffeS, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL].( 2015-03-02)[2020-04-27]. https:∥arxiv.org/abs/1502. 03167.

[19] Tu B, Yang X C, Li N Y, et al. Hyperspectral image classification via superpixel correlation coefficient representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(11): 4113-4127.