This is the implementation code for the paper, "A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos".
- Install the required dependencies: (for reference see how_to_install.pdf )
- Dataset: Download the initial dataset from here.
- weights: Download pre-trained resnet_50 coco weights and trained weights for bridge element segmentation from here.
- custom.py : this code is used for loading data and training the model
- Training.ipynb: loading the weight and calling the training function
- inference.ipynb: this code is used for inferencing.
- mrcnn/visualize.py : this code is used for visualizing the segmented bridge elements with mask.
If you use this repository, please cite the following paper:
@article{karim2021semi,
title={A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos},
author={Karim, Muhammad Monjurul and Qin, Ruwen and Chen, Genda and Yin, Zhaozheng},
journal={Structural Health Monitoring},
pages={14759217211010422},
year={2021},
publisher={SAGE Publications Sage UK: London, England}
}
Note that part of the codes are referred from Mask RCNN project.