[1] S BASAK, D B SHAKYAWAR, K K SAMANTA et al. Development of natural fibre based flexural composite： a sustainable mimic of natural leather. Materials Today Communications, 32, 103976(2022).
[2] 任龙芳, 赵国徽, 强涛涛. 超细纤维合成革仿天然皮革研究进展. 皮革科学与工程, 22, 36-40(2012).
L F REN, G H ZHAO, T T QIANG et al. Advances in the microfiber synthetic materials emulating natural leather. Leather Science and Engineering, 22, 36-40(2012).
[3] 李琛, 王冬, 仲鸿天. 超纤革水性含浸工艺的改进. 印染, 50, 36-39， 43(2024).
C LI, D WANG, H T ZHONG et al. Improvement of water-based impregnation process for microfiber leather. China Dyeing & Finishing, 50, 36-39， 43(2024).
[4] 张昕, 范浩军, 李静. 甲苯减量型超纤含浸用水性聚氨酯的制备与应用性能评价. 皮革科学与工程, 30, 1-8(2020).
X ZHANG, H J FAN, J LI et al. Preparation and application performance evaluation of toluene deweighting type microfiber impregnated waterborne polyurethane. Leather Science and Engineering, 30, 1-8(2020).
[5] B B ZHAO, X M QIAN, Y QIAN et al. Preparation of high-performance microfiber synthetic leather base using thermoplastic polyurethane/sulfonated polysulfone electrospun nanofibers. Textile Research Journal, 89, 2813-2820(2019).
[6] Y LIU, W JIANG, G D LI et al. Textile defect recognition network based on label embedding. Opt. Precision Eng., 31, 1563-1579(2023).
刘颖, 姜威, 李冠典. 基于标签嵌入方法的纺织品瑕疵识别网络. 光学 精密工程, 31, 1563-1579(2023).
[7] W LIU, D ANGUELOV, D ERHAN et al. SSD： Single Shot Multibox Detector, 21-37(2016).
[8] J TERVEN, D M CÓRDOVA-ESPARZA, J A ROMERO-GONZÁLEZ. A comprehensive review of YOLO architectures in computer vision： from YOLOv1 to YOLOv8 and YOLO-NAS. Machine Learning and Knowledge Extraction, 5, 1680-1716(2023).
[9] H S XIE, Y F ZHANG, Z S WU. An improved fabric defect detection method based on SSD. AATCC Journal of Research, 8, 181-190(2021).
[10] R LUO, R H CHEN, F T JIA et al. RBD-Net： robust breakage detection algorithm for industrial leather. Journal of Intelligent Manufacturing, 34, 2783-2796(2023).
[11] B LIU, H WANG, Z CAO et al. PRC-Light YOLO： An efficient lightweight model for fabric defect detection. Applied Sciences, 14, 938(2024).
[12] R AZAD, L NIGGEMEIER, M HÜTTEMANN et al. Beyond Self-Attention： Deformable Large Kernel Attention for Medical Image Segmentation, 1276-1286(2024).
[13] K W LAU, L M PO, Y A U REHMAN. Large separable kernel attention： rethinking the large kernel attention design in CNN. Expert Systems with Applications, 236, 121352(2024).
[14] W H WANG, J F DAI, Z CHEN et al. Internimage： exploring large-scale vision foundation models with deformable convolutions, 14408-14419(2023).
[15] W Z LIU, H LU, H T FU et al. Learning to upsample by learning to sample, 6004-6014(2023).
[16] W Z SHI, J CABALLERO, F HUSZÁR et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, 1874-1883(2016).
[17] J Q WANG, K CHEN, R XU et al. CARAFE： Content-Aware Reassembly of Features, 3007-3016(2019).
