/SAINet

SAINet

We propose a specificity autocorrelation integration network (SAINet) for surface defect detection of rails. Paper：https://www.sciencedirect.com/science/article/pii/S0143816623003913?dgcid=author

2024-Specificity autocorrelation integration network for surface defect detection of no-service rail.pdf

Code and Results of SAINet

We provide the source code of SAINet adn the resutls of our SAINet on the NEU RSDDS-AUG dataset and six other benchmark datasets. Download link: https://pan.baidu.com/s/1HjGwQot2QuBftGeB_9Ro8g?pwd=sxbr

NEU RSDDS-AUG Dataset

Download link: https://pan.baidu.com/s/1xKkyu0y2Z_NpOnf_203Z-w?pwd=u9yn

Citation

Yunhui Yan, Xiujian Jia, Kechen Song, Wenqi Cui, Ying Zhao, Chuang Liu, Jingbo Guo. Specificity Autocorrelation Integration Network for Surface Defect Detection of No-Service Rail [J]. Optics and Lasers in Engineering, 2024, 172, 107862.

Related works

Jingpeng Wang, Kechen Song, Defu Zhang, Menghui Niu, Yunhui Yan. Collaborative Learning Attention Network Based on RGB Image and Depth Image for Surface Defect Inspection of No-Service Rail [J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(6),4874-4884.

Menghui Niu, Kechen Song, Liming Huang, Qi Wang, Yunhui Yan, Qinggang Meng. Unsupervised Saliency Detection of Rail Surface Defects using Stereoscopic Images [J]. IEEE Transactions on Industrial Informatics, 2021,17(3),2271-2281.