Asymmetrical Contrastive Learning Network via Knowledge Distillation for No-Service Rail Surface Defect Detection
Python >=3.6
Pytorch >= 1.7.1
Cuda = 10.2
The results graphs of our tests are saved in the:
[Baidu Clould]https://pan.baidu.com/s/1d0poeuS5JQt4dDQ9FhibJg?pwd=cim9 提取码:cim9
All the training weights of our model will be saved in the:
[Baidu Clould] https://pan.baidu.com/s/13FeF2hgFtm6SrsEkhnT-lQ?pwd=dsy6 提取码:dsy6
Table I Evaluation metrics obtained from compared methods.
Model | Sm ↑ | MAE ↓ | maxFm ↑ | maxEm ↑ | FLOPs/G ↓ | Params/M ↓ |
---|---|---|---|---|---|---|
S2MA2020 | 0.775 | 0.141 | 0.817 | 0.864 | 108.1 | 86.7 |
EDR2020 | 0.811 | 0.082 | 0.850 | 0.893 | 32.2 | 39.3 |
BBS2020 | 0.828 | 0.074 | 0.867 | 0.909 | 12.7 | 49.8 |
HAI2021 | 0.718 | 0.171 | 0.803 | 0.829 | 73.5 | 59.8 |
EMI2021 | 0.800 | 0.104 | 0.850 | 0.876 | 106.9 | 99.1 |
SPNet2021 | 0.830 | 0.072 | 0.877 | 0.915 | 55.0 | 175.3 |
DAC2022 | 0.824 | 0.071 | 0.875 | 0.911 | 109.3 | 98.4 |
CSEP2022 | 0.814 | 0.085 | 0.866 | 0.899 | 45.4 | 18.8 |
CIR2022 | 0.809 | 0.086 | 0.856 | 0.894 | 17.3 | 103.2 |
RD3D2022 | 0.797 | 0.093 | 0.839 | 0.883 | 17.6 | 28.9 |
DRER2022 | 0.844 | 0.059 | 0.891 | 0.933 | 17.3 | 69.8 |
CLA2022 | 0.835 | 0.069 | 0.878 | 0.920 | 82.6 | 184.2 |
LENO2022 | 0.817 | 0.083 | 0.857 | 0.900 | 162.1 | 131.0 |
ICON2023 | 0.843 | 0.066 | 0.884 | 0.925 | 34.8 | 65.7 |
CAVER2023 | 0.809 | 0.090 | 0.848 | 0.891 | 63.0 | 93.8 |
ACLNet-T | 0.860 | 0.055 | 0.897 | 0.938 | 20.7 | 79.1 |
ACLNet-S | 0.842 | 0.063 | 0.883 | 0.926 | 5.6 | 11.9 |
ACLNet-S* | 0.856 | 0.056 | 0.896 | 0.936 | 5.6 | 11.9 |
Model | NJU2K | NLPR | STERE | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Sm ↑ | MAE ↓ | maxFm ↑ | maxEm ↑ | Sm ↑ | MAE ↓ | maxFm ↑ | maxEm ↑ | Sm ↑ | MAE ↓ | maxFm ↑ | maxEm ↑ | |
S2MA2020 | 0.894 | 0.053 | 0.930 | 0.889 | 0.915 | 0.030 | 0.953 | 0.902 | 0.890 | 0.051 | 0.932 | 0.882 |
EDR2020 | 0.839 | 0.076 | 0.883 | 0.823 | 0.886 | 0.038 | 0.928 | 0.860 | 0.851 | 0.065 | 0.899 | 0.831 |
BBS2020 | 0.921 | 0.035 | 0.949 | 0.920 | 0.930 | 0.023 | 0.961 | 0.918 | 0.908 | 0.041 | 0.942 | 0.903 |
HAI2021 | 0.912 | 0.038 | 0.944 | 0.915 | 0.921 | 0.024 | 0.960 | 0.915 | 0.907 | 0.040 | 0.944 | 0.906 |
EMI2021 | 0.881 | 0.050 | 0.924 | 0.876 | 0.916 | 0.026 | 0.953 | 0.902 | 0.897 | 0.042 | 0.938 | 0.894 |
DAC2022 | 0.890 | 0.046 | 0.929 | 0.887 | 0.913 | 0.025 | 0.949 | 0.897 | 0.899 | 0.043 | 0.936 | 0.892 |
MC2022 | 0.902 | 0.042 | 0.939 | 0.900 | 0.918 | 0.025 | 0.956 | 0.907 | 0.903 | 0.042 | 0.945 | 0.898 |
CIR2022 | 0.925 | 0.035 | 0.955 | 0.927 | 0.933 | 0.023 | 0.966 | 0.924 | 0.917 | 0.039 | 0.950 | 0.916 |
DRER2022 | 0.906 | 0.038 | 0.943 | 0.907 | 0.915 | 0.024 | 0.953 | 0.901 | 0.895 | 0.042 | 0.943 | 0.891 |
LENO2022 | 0.838 | 0.073 | 0.888 | 0.824 | 0.878 | 0.040 | 0.921 | 0.845 | 0.856 | 0.062 | 0.906 | 0.840 |
ICON2023 | 0.893 | 0.051 | 0.937 | 0.891 | 0.904 | 0.032 | 0.951 | 0.885 | 0.899 | 0.047 | 0.945 | 0.890 |
CAVER2023 | 0.926 | 0.030 | 0.958 | 0.928 | 0.934 | 0.021 | 0.970 | 0.928 | 0.918 | 0.033 | 0.955 | 0.916 |
ACLNet-T | 0.929 | 0.028 | 0.960 | 0.932 | 0.937 | 0.018 | 0.971 | 0.929 | 0.918 | 0.032 | 0.955 | 0.915 |
ACLNet-S | 0.911 | 0.038 | 0.949 | 0.910 | 0.927 | 0.023 | 0.965 | 0.918 | 0.916 | 0.036 | 0.952 | 0.909 |
ACLNet-S* | 0.913 | 0.037 | 0.951 | 0.914 | 0.930 | 0.021 | 0.966 | 0.919 | 0.917 | 0.035 | 0.953 | 0.911 |