Object Separation for X-ray Security Images
This repository contains the evaluation pipeline code for our paper entitled "Pixel-level Analysis for Improving Threat Detection in X-ray Security Images". We also provide the pixel-level annotations on a randomly sampled subset from the SIXray dataset as well as the list of mislabeled negative samples in the data folder.
This code was tested on an Ubuntu 18.04.5 machine with Python 3.7.11.
Please install detectron2, smp, and albumentations.
# install detectron2
git clone https://github.com/facebookresearch/detectron2.git detectron2_repo
pip install -e detectron2_repo
# install smp and albumentations
pip install -U segmentation-models-pytorch albumentations --user
To run the code:
python test_pipeline.py --img_path /path/to/image.jpg
Classification mAP on SIXray100 subset:
| Method | mean | Gun | Knife | Wrench | Pliers | Scissors |
|---|---|---|---|---|---|---|
| CHR | 64.54 | 98.60 | 81.09 | 46.69 | 62.72 | 33.61 |
| GBAD | 66.14 | 95.74 | 85.25 | 54.87 | 53.10 | 41.74 |
| Mask R-CNN | 86.55 | 98.74 | 77.72 | 78.35 | 89.78 | 77.11 |
| Proposed | 91.40 | 99.46 | 90.24 | 86.88 | 94.37 | 86.06 |