Python基于残差网络的带钢表面缺陷显着目标检测的编码器、解码器源码+数据集

# EDRNet source code for our IEEE TIM 2020 paper entitled [EDRNet: Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects](https://ieeexplore.ieee.org/document/9116810) (**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. run`python 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](https://drive.google.com/file/d/1yQdfow1-WvDilQTZ1zj1EbbErN1DksVF/view?usp=sharing), then Unzip it to the directory of `./Data` 2. download the pre-trained model [EDRNet_epoch_600.pth](https://drive.google.com/file/d/1FJe9j-F7r3kdlEJgBC-Izi37ANytwLF-/view?usp=sharing), 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. run`python 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](https://www.sciencedirect.com/science/article/abs/pii/S0143816619317361)** ## Results We also provide the experimental results of all the comparative methods in our paper.([Results](https://drive.google.com/file/d/1XAFLIPbgJQpX2QiL2JZtnoK0QY2ARWTn/view?usp=sharing)) **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](https://drive.google.com/drive/folders/1_ZHQabyt-ndrLnAOauXUqov9Yxp5Gsyl?usp=sharing)) BaiduYun Drive: (**link:https://pan.baidu.com/s/1jw8jHEpa_AWgf2rMpmsebQ code:c9gb**) - mat_Results.zip - NoisyImages.zip - NoisyTestResults.zip ## Performance Preview **Visual comparison**  **Quantitative comparison**  ## 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} } ```