/EDRNet

⚡EDRNet:Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects

Primary LanguagePython

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

  1. cd to ./Data, and Unzip the file of trainingDataset.zip into this folder.
  2. path of training images:./Data/trainingDataset/imgs_train/ path of training labels:./Data/trainingDataset/masks_train/
  3. runpython edrnet_train.py to start training
  4. the trained model will be saved in ./trained_models

Testing

  1. download the test dataset SD-saliency-900.zip, then Unzip it to the directory of ./Data
  2. download the pre-trained model EDRNet_epoch_600.pth, then put it to the directory of ./trained_models
  3. path of testing dataset: ./Data/SD-saliency-900/imgs/ path of pre-trained model: ./trained_models/EDRNet_epoch_600.pth
  4. runpython edrnet_test.py to start testing
  5. 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

Visual comparison visual_comparison.jpg

Quantitative comparison quantitative_evaluation.png

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}
}