References
- Bolya, D., Zhou, C., Xiao, F., & Lee, Y. J. (Eds.). (2018). YOLACT: Real-Time Instance Segmentation. International Conference on Computer Vision.
- Chen, H., Sun, K., Tian, Z., Shen, C., & Yan, Y. (2020). Blendmask: Top-down meets bottom-up for instance segmentation [Paper presentation]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR42600.2020.00860
- Chen, W., Xu, Z. B., Liu, S., Li, C. Q., & Song, C. S. (2018). Research on the segmentation method of cross fiber. Cotton Textile Technology, 46(12), 13–17.
- Chen, X. C., Zhang, Y. B., Fu, S., Peng, H., & Chen, X. (2017). Preprocessing and Segmentation Algorithm for Multiple Overlapped Fiber Image. International Conference on Image and Graphics.
- Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., & Fang, Z. (2020). Dual Attention Network for Scene Segmentation [Paper presentation]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
- Gao, F., Lin, J., Liu, H., & Lu, S. (2019). A novel VBM framework of fiber recognition based on image segmentation and DCNN. IEEE Transactions on Instrumentation and Measurement, 69(4), 963–973.
- He, K., Gkioxari, G., Dollar, P., & Girshick, R. (Eds.). (2017). Mask R-CNN [Paper presentation]. 2017 IEEE International Conference on Computer Vision (ICCV) https://doi.org/10.1109/ICCV.2017.322
- Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS (Vol. 25). Curran Associates Inc.
- Li, Y., Qi, H., Dai, J., Ji, X., & Wei, Y. (Eds.). (2017). Fully Convolutional Instance- Aware Semantic Segmentation. Computer Vision & Pattern Recognition.
- Lin, T. Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature Pyramid Networks for Object Detection [Paper presentation]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society. https://doi.org/10.1109/CVPR.2017.106
- Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., & Ramanan, D. (2014). Microsoft COCO: Common Objects in Context.
- Liu, B., Zhang, X., Gao, Z., & Li, C. (2017). Weld Defect Images Classification with VGG16-Based Neural Network. Springer.
- Luo, J., Lu, K., Chen, Y., & Zhang, B. (2021). Automatic identification of cashmere and wool fibers based on microscopic visual features and residual network model. Micron (Oxford, England: 1993), 143(21), 103023.
- Luo, J., Lu, K., Zhong, Y., Zhang, B., & Lv, H. (2021). Cashmere and wool identification based on convolutional neural network. Journal of Engineered Fibers and Fabrics, 16, 155892502110050. https://doi.org/10.1177/15589250211005088
- Mayya, V., Pai, R. M., & Pai, M. (2016). Automatic facial expression recognition using dcnn. Procedia Computer Science, 93, 453–461. https://doi.org/10.1016/j.procs.2016.07.233
- Nayak, R., Houshyar, S., Khandual, A., Padhye, R., & Fergusson, S. (2015). Identification of natural textile fibers. Handbook of Natural Fibers, 1, 503–534.
- Peng, H., Xue, C., Shao, Y., Chen, K., Xiong, J., Xie, Z., & Zhang, L. (2020). Semantic segmentation of litchi branches using deeplabv3+ model. IEEE Access, 8, 164546–164555. https://doi.org/10.1109/ACCESS.2020.3021739
- Shang, S., Liu, Y., Yi, H., & Zhang, Y. (2010). The research on identification of wool or cashmere fiber based on the digital image. International Conference on Machine Learning & Cybernetics. IEEE, 2, 833–838.
- Tian, Z., Shen, C., Chen, H., & He, T. (Eds.). (2020). FCOS: Fully Convolutional One-Stage Object Detection [Paper presentation]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/ICCV.2019.00972
- Wu, Z., Shen, C., & Hengel, A. (2016). Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recognition, 90, 119–133.
- Xing, W. Y., Deng, N., Xin, B. J., & Yu, C. (2019). Identification of wool and cashmere based on multi-feature fusion image analysis technology. Journal of Textile Research, 40(3), 146–152.
- Xing, W., Liu, Y., Deng, N., Xin, B., Wang, W., & Chen, Y. (2020). Automatic identification of cashmere and wool fibers based on the morphological features analysis. Micron, 128, 102768–104328. https://doi.org/10.1016/j.micron.2019.102768
- Yildiz, K. (2019). Identification of wool and mohair fibers with texture feature extraction and deep learning. IET Image Processing, 14(2), 348–353. https://doi.org/10.1049/iet-ipr.2019.0907
- Yuan, N. (2015). Study on the separation method of overlap rods based on digital image processing [M.D. diss]. Shandong University of Technology.