[1] Wei Li, Quanfeng Guo. Application of carbon fiber composites to cosmonautic fields. Chinese Optics, 4, 201-212(2011).

[2] Costanzo Bellini, Cocco Vittorio Di, Francesco Iacoviello, et al. Performance evaluation of CFRP/Al fibre metal laminates with different structural characteristics. Composite Structures, 225, 111117(2019).

[3] Norbert Forintos, Tibor Czigány. Multifunctional application of carbon fiber reinforced polymer composites: Electrical properties of the reinforcing carbon fibers-A short review. Composites Part B, 162, 331-343(2019).

[4] J Brüning, B Denkena, M A Dittrich, et al. Machine learning approach for optimization of automated fiber placement processes. Procedia CIRP, 66, 74-78(2017).

[5] Nima Bakhshi, Mehdi Hojjati. An experimental and simulative study on the defects appeared during tow steering in automated fiber placement. Composites Part A: Applied Science and Manufacturing, 113, 122-131(2018).

[6] Todd Rudberg, Justin Nielson, Mike Henscheid, et al. Improving AFP cell performance. SAE International Journal of Aerospace, 7, 317-321(2014).

[7] Liwei Wen, Qinghua Song, Lihua Qin, et al. Defect detection and closed-loop control system for automated fiber placement forming components based on machine vision and UMAC. Acta Aeronautica ET Astronautica Sinica, 36, 3991-4000(2015).

[8] [8] Wei Tianshu. Research on image detection method f defects of composite prepreg tapes[D]. Jinan: Shong University of Technology, 2018. (in Chinese)

[9] Berend Denkena, Carsten Schmidt, Klaas Völtzer, et al. Thermographic online monitoring system for Automated Fiber Placement processes. Composites Part B, 97, 239-243(2016).

[10] Carsten Schmidt, Berend Denkena, Klaas Völtzer, et al. Thermal image-based monitoring for the Automated Fiber Placement process. Procedia CIRP, 62, 27-32(2017).

[11] Bo Wang, Ruifeng Tian. Judgement of critical state of water film rupture on corrugated plate wall based on SIFT feature selection algorithm and SVM classification method. Nuclear Engineering and Design, 347, 132-139(2019).

[12] Liping Yang, Alan M MacEachren, Prasenjit Mitra, et al. Visually-enabled active deep learning for (Geo) text and image classification: A review. ISPRS International Journal of Geo-Information, 7, 65(2018).

[13] [13] Zhang Yuting, Sohn Kihyuk, Villegas Ruben, et al. Improving object detection with deep convolutional wks via bayesian optimization structured prediction[C]2015 IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2015: 249258.

[14] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[15] Carsten Schmidt, Tristan Hocke, Berend Denkena. Artificial intelligence for non-destructive testing of CFRP prepreg materials. Production Engineering, 13, 617-626(2019).

[16] Christopher Sacco, Anis Baz Radwan, Andrew Anderson, et al. Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection. Composite Structures, 250, 112514(2020).

[17] Hei Law, Jia Deng. CornerNet: Detecting objects as paired keypoints. International Journal of Computer Vision, 128, 642-656(2020).

[18] [18] Zhou Xingyi, Zhuo Jiacheng, Krähenbühl Philipp. Bottomup object detection by grouping extreme center points[C] 2019 IEEE CVF Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2019: 850859.

[19] Xingyi Zhou, Dequan Wang, Philipp Krähenbühl. Objects as points. arXiv, 1904.07850(2019).

[20] [20] Lin TsungYi, Dollár Piotr, Girshick Ross, et al. Feature pyra wks f object detection[C]2017 IEEE Conference on Computer Vision Pattern Recognition (CVPR), IEEE, 2017: 936944.

[21] [21] Liu Songtao, Huang Di, Wang Yunhong. Learning spatial fusion f singleshot object detection[J]. arXiv preprint, 2019: 1911.09516.

[22] [22] Howard rew, Sler Mark, Chu Grace, et al. Searching f MobileV3[J]. arXiv preprint,2019: 1905.02244.

[23] [23] Howard rew, Zhu Menglong, Chen Bo, et al. Mobiles: Efficient convolutional neural wks f mobile vision applications[J]. arXiv preprint, 2017: 1704.04861.

[24] [24] Sler Mark, Howard rew, Zhu Menglong, et al. MobileV2: Inverted residuals linear bottlenecks[J]. arXiv preprint, 2018: 1801.04381.