[1] Li Wei, Guo Quanfeng. Application of carbon fiber composites to cosmonautic fields [J]. Chinese Optics, 2011, 4(3): 201-212. (in Chinese) doi:  10.3969/j.issn.2095-1531.2011.03.001
[2] Bellini Costanzo, Di Cocco Vittorio, Iacoviello Francesco, et al. Performance evaluation of CFRP/Al fibre metal laminates with different structural characteristics [J]. Composite Structures, 2019, 225: 111117. doi:  10.1016/j.compstruct.2019.111117
[3] Forintos Norbert, Czigány Tibor. Multifunctional application of carbon fiber reinforced polymer composites: Electrical properties of the reinforcing carbon fibers-A short review [J]. Composites Part B, 2019, 162: 331-343. doi:  10.1016/j.compositesb.2018.10.098
[4] Brüning J, Denkena B, Dittrich M A, et al. Machine learning approach for optimization of automated fiber placement processes [J]. Procedia CIRP, 2017, 66: 74-78. doi:  10.1016/j.procir.2017.03.295
[5] Bakhshi Nima, Hojjati Mehdi. An experimental and simulative study on the defects appeared during tow steering in automated fiber placement [J]. Composites Part A: Applied Science and Manufacturing, 2018, 113: 122-131. doi:  10.1016/j.compositesa.2018.07.031
[6] Rudberg Todd, Nielson Justin, Henscheid Mike, et al. Improving AFP cell performance [J]. SAE International Journal of Aerospace, 2014, 7(2): 317-321. doi:  10.4271/2014-01-2272
[7] Wen Liwei, Song Qinghua, Qin Lihua, et al. Defect detection and closed-loop control system for automated fiber placement forming components based on machine vision and UMAC [J]. Acta Aeronautica ET Astronautica Sinica, 2015, 36(12): 3991-4000. (in Chinese) doi:  10.7527/S1000-6893.2015.0243
[8] 魏天舒. 复合材料预浸带缺陷的图像检测方法研究[D]. 山东: 山东理工大学, 2018.

Wei Tianshu. Research on image detection method for defects of composite prepreg tapes[D]. Jinan: Shandong University of Technology, 2018. (in Chinese)
[9] Denkena Berend, Schmidt Carsten, Völtzer Klaas, et al. Thermographic online monitoring system for Automated Fiber Placement processes [J]. Composites Part B, 2016, 97: 239-243. doi:  10.1016/j.compositesb.2016.04.076
[10] Schmidt Carsten, Denkena Berend, Völtzer Klaas, et al. Thermal image-based monitoring for the Automated Fiber Placement process [J]. Procedia CIRP, 2017, 62: 27-32. doi:  10.1016/j.procir.2016.06.058
[11] Wang Bo, Tian Ruifeng. Judgement of critical state of water film rupture on corrugated plate wall based on SIFT feature selection algorithm and SVM classification method [J]. Nuclear Engineering and Design, 2019, 347: 132-139. doi:  10.1016/j.nucengdes.2019.03.025
[12] Yang Liping, MacEachren Alan M, Mitra Prasenjit, et al. Visually-enabled active deep learning for (Geo) text and image classification: A review [J]. ISPRS International Journal of Geo-Information, 2018, 7(2): 65. doi:  10.3390/ijgi7020065
[13] Zhang Yuting, Sohn Kihyuk, Villegas Ruben, et al. Improving object detection with deep convolutional networks via bayesian optimization and structured prediction[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015: 249-258.
[14] Chen Liang-Chieh, Papandreou George, Kokkinos Iasonas, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. doi:  10.1109/TPAMI.2017.2699184
[15] Schmidt Carsten, Hocke Tristan, Denkena Berend. Artificial intelligence for non-destructive testing of CFRP prepreg materials [J]. Production Engineering, 2019, 13(5): 617-626. doi:  10.1007/s11740-019-00913-3
[16] Sacco Christopher, Radwan Anis Baz, Anderson Andrew, et al. Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection [J]. Composite Structures, 2020, 250: 112514. doi:  10.1016/j.compstruct.2020.112514
[17] Law Hei, Deng Jia. CornerNet: Detecting objects as paired keypoints [J]. International Journal of Computer Vision, 2020, 128: 642-656. doi:  10.1007/s11263-019-01204-1
[18] Zhou Xingyi, Zhuo Jiacheng, Krähenbühl Philipp. Bottom-up object detection by grouping extreme and center points[C]// 2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2019: 850-859.
[19] Zhou Xingyi, Wang Dequan, Krähenbühl Philipp. Objects as points [J]. arXiv, 2019: 1904.07850.
[20] Lin Tsung-Yi, Dollár Piotr, Girshick Ross, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2017: 936-944.
[21] Liu Songtao, Huang Di, Wang Yunhong. Learning spatial fusion for single-shot object detection[J]. arXiv preprint, 2019: 1911.09516.
[22] Howard Andrew, Sandler Mark, Chu Grace, et al. Searching for MobileNetV3[J]. arXiv preprint,2019: 1905.02244.
[23] Howard Andrew, Zhu Menglong, Chen Bo, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint, 2017: 1704.04861.
[24] Sandler Mark, Howard Andrew, Zhu Menglong, et al. MobileNetV2: Inverted residuals and linear bottlenecks[J]. arXiv preprint, 2018: 1801.04381.