参考文献/References:
[1] 史先传, 徐镇冬, 苏胜辉, 等. 整纬设备光电检测头的设计与研究[J]. 传感技术学报, 2019, 32(8): 1169-1174.
SHI Xianchuan, XU Zhendong, SU Shenghui, et al. Design and research on photoelectric detector of weft-straightener[J]. Chinese Journal of Sensors and Actuators, 2019, 32(8): 1169-1174.(in Chinese)
[2] LEE J Y, BAE G H, CHUNG Y S, et al. Analysis and control of camera type weft straightener[J]. World Academy of Science, Engineering and Technology, 2017, 11(3): 567-571.
[3] 王奇锴, 潘如如, 高卫东, 等. 基于图像处理的牛仔织物纬斜检测方法[J]. 棉纺织技术, 2020, 48(6): 31-35.
WANG Qikai, PAN Ruru, GAO Weidong, et al. Test method of denim fabric bias filling based on image processing[J]. Cotton Textile Technology, 2020, 48(6): 31-35.(in Chinese)
[4] 史先传, 董冲, 苏胜辉, 等. 结合Sobel和PPHT的织物纬斜检测方法研究[J]. 计算机测量与控制, 2020, 28(8): 48-52, 57.
SHI Xianchuan, DONG Chong, SU Shenghui, et al. Research on fabric weft skew detection method using Sobel and PPHT[J]. Computer Measurement and Control, 2020, 28(8): 48-52, 57.(in Chinese)
[5] MEI S A, WANG Y D, WEN G J. Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model[J]. Sensors, 2018, 18(4): 1064.
[6] ZHAO Z X, LI B, DONG R, et al. A surface defect detection method based on positive samples[C]//Pacific Rim International Conference on Artificial Intelligence. Cham: Springer, 2018: 473- 481.
[7] LIU J H, WANG C Y, SU H, et al. Multistage GAN for fabric defect detection[J]. IEEE Transactions on Image Processing, 2020, 29: 3388-3400.
[8] ZHANG H W, QIAO G H, LU S A, et al. Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection[J]. Textile Research Journal, 2023, 93(5/6): 1178-1195.
[9] TSAI D M, JEN P H. Autoencoder-based anomaly detection for surface defect inspection[J]. Advanced Enginee-ring Informatics, 2021, 48: 101272.
[10] XU R G, HAO R Y, HUANG B Q. Efficient surface defect detection using self-supervised learning strategy and segmentation network[J]. Advanced Engineering Informatics, 2022, 52: 101566.
[11] FANG J C, LIU Q, LI J Z. A deployment scheme of YOLO v5 with inference optimizations based on the triton inference server[C]//2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics(ICCCBDA). Chengdu: IEEE, 2021: 441- 445.
[12] 钱炜. 基于神经网络的织物疵点检测研究[D]. 上海: 东华大学, 2018.
[13] 天池. 布匹瑕疵检测数据集[EB/OL].(2020-10-21)[2023-03-30]. https://tianchi.aliyun.com/dataset/dataDetail?dataId=79336.
[14] ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
[15] WERMAN M. Affine invariants[M]//Computer Vision. Cham: Springer International Publishing, 2020: 1-3.
[16] 刘建宝. 面向摄像整纬器的织物纬斜检测与整纬研究[D]. 泉州: 华侨大学, 2020.
[17] SUN C, SHRIVASTAVA A, SINGH S, et al. Revisiting unreasonable effectiveness of data in deep learning era[C]//2017 IEEE International Conference on Computer Vision(ICCV). Venice: IEEE, 2017: 843-852.
[18] DUMOULIN V, VISIN F. A guide to convolution arith-metic for deep learning[EB/OL].(2018-01-12)[2023-03-30]. https://arxiv.org/abs/1603.07285.
[19] VIRAKTAMATH D S, NAVALGI P, NEELOPANT A. Comparison of YOLO v3 and SSD algorithms[J]. International Journal of Engineering Research and Technology, 2021, 10(2): 193-196.
[20] 侯谕融, 狄岚, 梁久祯. 融合高斯金字塔特征的低分辨率人脸识别[J]. 小型微型计算机系统, 2021, 42(10): 2107-2115.
HOU Yurong, DI Lan, LIANG Jiuzhen. Fusion of Gaussian image pyramid feature for low-resolution face recognition[J]. Journal of Chinese Computer Systems, 2021, 42(10): 2107-2115.(in Chinese)
[21] YU J H, JIANG Y N, WANG Z Y, et al. UnitBox: an advanced object detection network[C]//Proceedings of the 24th ACM international conference on Multimedia. New York: ACM, 2016: 516-520.
(责任编辑:沈天琦)