[1] National Quality Supervision. Inspection and Quarantine Bureau. Fine cotton cotton: GB 1103—2012[S]. Beijing: National Standardization Management Committee(2012).
[2] Gai W Q, Xu L, Ding Y D[J]. Causes, classification, harm and solutions of foreign fibers in cotton China Fiber Inspection, 2017, 85-86.
[3] Zhou Z C. Construction of the evaluation model of the content grade of foreign fiber[D]. Tianjin: Tianjin Polytechnic University(2019).
[4] Luo Y H, Du Y H, Liu R J et al. Discussion on rough set theory for classification and recognition of foreign fiber in cotton[J]. Cotton Textile Technology, 43, 73-76(2015).
[5] Wei P, Zhang L, Liu X et al. Detecting method of foreign fibers in seed cotton using double illumination imaging[J]. Journal of Textile Research, 38, 32-38(2017).
[6] Shi H Y, Guan S Q. Cotton foreign fibers detection based on visual attention computational model[J]. Journal of Donghua University(Natural Science), 42, 400-405(2016).
[7] He X Y, Wei P, Zhang L et al. Detection method of foreign fibers in seed cotton based on deep-learning[J]. Journal of Textile Research, 39, 131-135(2018).
[8] Zhang L, Wei P, Wu J B et al. Detection method of foreign fibers in cotton based on illumination of line-laser and LED[J]. Transactions of the CSAE, 32, 289-293(2016).
[9] Yang W Z, Li D L, Wei X H et al. AVI system for classification of foreign fibers in cotton[J]. Transactions of the Chinese Society for Agricultural Machinery, 40, 177-181, 227(2009).
[10] Wang J F, Du Y H, Jiang X M et al. Classification processing method of cotton foreign fibers based on probability statistics and BP neural network[J]. Applied Mechanics and Materials, 598, 428-431(2014).
[11] Liu S X, Wang J X, Zheng W X et al. Classification method of adaptive threshold segmentation algorithm and moment for foreign fibers in cotton[J]. Transactions of the CSAE, 25, 320-324(2009).
[12] Zhang M Y, Li C Y, Yang F Z. Classification of foreign matter embedded inside cotton lint using short wave infrared (SWIR) hyperspectral transmittance imaging[J]. Computers and Electronics in Agriculture, 139, 75-90(2017).
[13] Wang J X, Li H B, Wang R et al. A fast feature selection for cotton foreign fiber objects based on BPSO[J]. Transactions of the Chinese Society for Agricultural Machinery, 44, 188-191(2013).
[14] Du Y H, Ma T, Yang C W et al. Detection clustering analysis algorithm and system parameters study of the near-point multi-class foreign fiber[J]. The Journal of the Textile Institute, 108, 1022-1027(2017).
[15] Tan W X, Zhao C J, Wu H R et al. A deep learning network for recognizing fruit pathologic images based on flexible momentum[J]. Transactions of the Chinese Society for Agricultural Machinery, 46, 20-25(2015).
[16] Zhao D A, Liu X Y, Sun Y P et al. Detection of underwater crabs based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 50, 151-158(2019).
[17] Xiong J T, Liu Z, Tang L Y et al. Visual detection technology of green citrus under natural environment[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 45-52(2018).
[18] Sun Y, Zhou Y, Yuan M S et al. UAV real-time monitoring for forest pest based on deep learning[J]. Transactions of the CSAE, 34, 74-81(2018).
[19] Yang W, Wang H Y, Zhang J et al. An improved vehicle real-time detection algorithm based on Faster-RCNN[J]. Journal of Nanjing University (Natural Sciences), 55, 231-237(2019).
[20] Tang C, Ling Y S, Yang H et al. Decision-level fusion detection for infrared and visible spectra based on deep learning[J]. Infrared and Laser Engineering, 48, 0626001(2019).
[22] Lin G, Wang B, Peng H et al. Multi-target detection and location of transmission line inspection image based on improved Faster-RCNN[J]. Electric Power Automation Equipment, 39, 213-218(2019).
[24] Li D J, Li R H. Research on the mugs defect detection method based on improved Faster RCNN[J]. Laser & Optoelectronics Progress, 57, 031502(2020).
[30] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).
[31] Lattanzi S, Lavastida T, Moseley B et al. Online scheduling via learned weights[M]. ∥Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 1859-1877(2020).
[32] Everingham M. Eslami S, van Gool L, et al. The pascal visual object classes challenge: a retrospective[J]. International Journal of Computer Vision, 111, 98-136(2015).