BBS-Net

BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network

Figure 1: Pipeline of the BBS-Net.

1. Requirements

Python 3.7, Pytorch 0.4.0+, Cuda 10.0, TensorboardX 2.0, opencv-python

2. Data Preparation

• Download the raw data from Here [code: yiy1] and trained model (BBSNet.pth) from Here [code: dwcp]. Then put them under the following directory:

   -BBS_dataset\ 
     -RGBD_for_train\  
     -RGBD_for_test\
     -test_in_train\
   -BBSNet
     -models\
     -model_pths\
        -BBSNet.pth
     ...
  

• Note that the depth maps of the raw data above are not normalized. If you train and test using the normalized depth maps, the performance will be improved.

3. Training & Testing

• Train the BBSNet:

  python BBSNet_train.py --batchsize 10 --gpu_id 0

• Test the BBSNet:

  python BBSNet_test.py --gpu_id 0

  The test maps will be saved to './test_maps/'.

• Evaluate the result maps:

  You can evaluate the result maps using the tool in here.

• If you need the codes using VGG16 and VGG19 backbones, please send to the email ([email protected]). Please provide your Name & Institution. Please note the code can be only used for research purpose.

4. Results

4.1 Qualitative Comparison

Figure 2: Qualitative visual comparison of the proposed model versus 8 SOTA models.

Table 1: Quantitative comparison of models using S-measure max F-measure, max E-measureand MAE scores on 7 datasets.

4.2 Results of multiple backbones

Table 2: Performance comparison using different backbones.

4.3 Download

• Test maps of the above datasets (ResNet50 backbone) can be download from here [code: qgai ].
• Test maps of vgg16 and vgg19 backbones of our model can be download from here [code: zuds ].
• Test maps of DUT-RGBD dataset (using the proposed training-test splits of DMRA) can be downloaded from here [code: 3nme ].

5. Citation

Please cite the following paper if you use this repository in your reseach.

@inproceedings{fan2020bbsnet,
title={BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network},
author={Fan, Deng-Ping and Zhai, Yingjie and Borji, Ali and Yang, Jufeng and Shao, Ling},
booktitle={ECCV},
year={2020}
}

• For more information about BBS-Net, please read the Manuscript (PDF) (Chinese version[code:0r4a]).

6. Benchmark RGB-D SOD

The complete RGB-D SOD benchmark can be found in this page:

http://dpfan.net/d3netbenchmark/

7. Acknowledgement

We implement this project based on the code of ‘Cascaded Partial Decoder for Fast and Accurate Salient Object Detection, CVPR2019’ proposed by Wu et al.