| An end-to-end steel surface defect detection approach via fusing multiple hierarchical features Y He, K Song, Q Meng, Y Yan IEEE transactions on instrumentation and measurement 69 (4), 1493-1504, 2019 | 754 | 2019 |
| A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects K Song, Y Yan Applied Surface Science 285, 858-864, 2013 | 626 | 2013 |
| PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection H Dong, K Song, Y He, J Xu, Y Yan, Q Meng IEEE Transactions on Industrial Informatics 16 (12), 7448-7458, 2019 | 346 | 2019 |
| EDRNet: Encoder–decoder residual network for salient object detection of strip steel surface defects G Song, K Song, Y Yan IEEE Transactions on Instrumentation and Measurement 69 (12), 9709-9719, 2020 | 173 | 2020 |
| Triplet-graph reasoning network for few-shot metal generic surface defect segmentation Y Bao, K Song, J Liu, Y Wang, Y Yan, H Yu, X Li IEEE Transactions on Instrumentation and Measurement 70, 1-11, 2021 | 146 | 2021 |
| Semi-supervised defect classification of steel surface based on multi-training and generative adversarial network Y He, K Song, H Dong, Y Yan Optics and Lasers in Engineering 122, 294-302, 2019 | 135 | 2019 |
| Research and perspective on local binary pattern S Ke-Chen, YAN Yun-Hui, C Wen-Hui, X Zhang Acta Automatica Sinica 39 (6), 730-744, 2013 | 107 | 2013 |
| Unsupervised saliency detection of rail surface defects using stereoscopic images M Niu, K Song, L Huang, Q Wang, Y Yan, Q Meng IEEE Transactions on Industrial Informatics 17 (3), 2271-2281, 2020 | 102 | 2020 |
| CGFNet: Cross-guided fusion network for RGB-T salient object detection J Wang, K Song, Y Bao, L Huang, Y Yan IEEE Transactions on Circuits and Systems for Video Technology 32 (5), 2949-2961, 2021 | 97 | 2021 |
| Semi-supervised anomaly detection with dual prototypes autoencoder for industrial surface inspection J Liu, K Song, M Feng, Y Yan, Z Tu, L Zhu Optics and Lasers in Engineering 136, 106324, 2021 | 84 | 2021 |
| Saliency detection for strip steel surface defects using multiple constraints and improved texture features G Song, K Song, Y Yan Optics and Lasers in Engineering 128, 106000, 2020 | 75 | 2020 |
| MCnet: Multiple context information segmentation network of no-service rail surface defects D Zhang, K Song, J Xu, Y He, M Niu, Y Yan IEEE Transactions on Instrumentation and Measurement 70, 1-9, 2020 | 74 | 2020 |
| 局部二值模式方法研究与展望 宋克臣， 颜云辉， 陈文辉， 张旭 自动化学报 39 (6), 730-744, 2013 | 72 | 2013 |
| Automatic recognition of surface defects on hot-rolled steel strip using scattering convolution network K Song, S Hu, Y Yan J. Comput. Inf. Syst 10 (7), 3049-3055, 2014 | 70 | 2014 |
| Vision-based automatic detection of steel surface defects in the cold rolling process: considering the influence of industrial liquids and surface textures YJ Zhao, YH Yan, KC Song The International Journal of Advanced Manufacturing Technology 90, 1665-1678, 2017 | 68 | 2017 |
| Adjacent evaluation of local binary pattern for texture classification K Song, Y Yan, Y Zhao, C Liu Journal of Visual Communication and Image Representation 33, 323-339, 2015 | 64 | 2015 |
| Deep metric learning-based for multi-target few-shot pavement distress classification H Dong, K Song, Q Wang, Y Yan, P Jiang IEEE Transactions on Industrial Informatics 18 (3), 1801-1810, 2021 | 62 | 2021 |
| RENet: Rectangular convolution pyramid and edge enhancement network for salient object detection of pavement cracks Y Wang, K Song, J Liu, H Dong, Y Yan, P Jiang Measurement 170, 108698, 2021 | 60 | 2021 |
| Two deep learning networks for rail surface defect inspection of limited samples with line-level label D Zhang, K Song, Q Wang, Y He, X Wen, Y Yan IEEE Transactions on Industrial Informatics 17 (10), 6731-6741, 2020 | 59 | 2020 |
| Fast 3D shape measurement using Fourier transform profilometry without phase unwrapping K Song, S Hu, X Wen, Y Yan Optics and Lasers in Engineering 84, 74-81, 2016 | 58 | 2016 |
Yunhui Yan (颜云辉)Northeastern UniversityVerified email at mail.neu.edu.cn
