EDRNet
source code for our IEEE TIM 2020 paper entitled EDRNet: Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects (DOI:10.1109/TIM.2020.3002277) by Guorong Song, Kechen Song and Yunhui Yan.
Requirement
- Python 3.6
- Pytorch 0.4.1 or 1.0.1(default)
- numpy
- torchvision
- glob
- PIL
- scikit-image
This code is tested on Ubuntu 16.04.
Training
- cd to
./Data
, and Unzip the file oftrainingDataset.zip
into this folder. - path of training images:
./Data/trainingDataset/imgs_train/
path of training labels:./Data/trainingDataset/masks_train/
- run
python edrnet_train.py
to start training - the trained model will be saved in
./trained_models
Testing
- download the test dataset SD-saliency-900.zip, then Unzip it to the directory of
./Data
- download the pre-trained model EDRNet_epoch_600.pth, then put it to the directory of
./trained_models
- path of testing dataset:
./Data/SD-saliency-900/imgs/
path of pre-trained model:./trained_models/EDRNet_epoch_600.pth
- run
python edrnet_test.py
to start testing - the predict results will be saved in
./Data/test_results/
Note: If you use SD-saliency-900
dataset in your paper, please cite Saliency detection for strip steel surface defects using multiple constraints and improved texture features
Results
We also provide the experimental results of all the comparative methods in our paper.(Results)
You can also download all the files including SD-saliency-900.zip, EDRNet_epoch_600.pth, Results
in BaiduYun Drive.(link:https://pan.baidu.com/s/1RSgkzNKxXA11ajtoFnk6Mw code: z91m)
Supplement
Here, we provide the results tested on Noisy Images with Salt and Pepper noise. (GoogleDrive) BaiduYun Drive: (link:https://pan.baidu.com/s/1jw8jHEpa_AWgf2rMpmsebQ code:c9gb)
- mat_Results.zip
- NoisyImages.zip
- NoisyTestResults.zip
Performance Preview
Citation
@InProceedings{SGR_2020_TIM,
author = {Song, Guorong and Song, Kechen and Yan, Yunhui},
title = {EDRNet: Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects},
booktitle = {IEEE Transactions on Instrumentation & Measurement (IEEE TIM)},
month = {June},
year = {2020}
}