Awesome

[Towards]

This repository is the official HiXray dataset.

HiXray Dataset Download Link: Please go to the webpage and download according to the prompts.

HiXray dataset:

LIM framework:

MuBo

Dataset
Install
Results
Acknowledgement

Dataset

Download link

(China mainland, BaiduNetdisk) https://pan.baidu.com/s/1ugMhHtEp4G7yEF_WbLt8DA password: buaa

(Other area, Google Drive) https://drive.google.com/drive/folders/1zlDLw36SQp5Y9Oi-TzUmZc4zFKk6nHgW?usp=sharing

In this project, we build the largest High-quality X-ray Security Inspection dataset.

All images of HiXray dataset are annotated manually by professional inspectors from an international airport, and the standard of annotating is based on the standard of training security inspectors.
HiXray dataset contains a total of 45364 X-ray images(36295 for training, 9069 for testing), including 8 categories of cutters, namely, 'Portable_Charger_1','Portable_Charger_2','Mobile_Phone','Laptop','Tablet','Cosmetic','Water','Nonmetallic_Lighter'
<div align=center> <img src="images/statistics.png"/> </div>
The information structure of annotation file is as follows: image name, category, top-left position of prohibited item (x1, y1), bottom-right position of prohibited item (x2, y2).
Comparison of existing open-source X-ray datasets:
<div align=center> <img src="images/dataset_comparison.png"/> </div>

Install

If you want to train your model, execute the following command: change root to MuBo
```
cd MuBo
```
Change the value of HiXray_ROOT variable in MuBo/data/HiXray.py file to the path where the training set is located, for example,
```
HiXray_ROOT = "/mnt/cvpr_dataset/train/"
```
run train.py
```
python train.py --save_folder /mnt/model/Mubo/save/ --image_sets /mnt/cvpr_dataset/train/train_name.txt --transfer /mnt/ssd300_mAP_77.43_v2.pth
```
save_folder is used to save the weight file obtained by training the model,

image_sets is the path to a TXT file that saves all the picture names used for training,

transfer indicates the pre-trained weight of SSD on VOC0712 (available at here).
If you want to test our model, execute the following command: change root to MuBo
```
cd MuBo
```
Change the value of HiXray_ROOT variable in MuBo/data/HiXray.py file to the path where the testing set is located, for example,
```
HiXray_ROOT = "/mnt/cvpr_dataset/test/"
```
run test.py
```
python test.py --trained_model /mnt/model/Mubo/weights/Mubo.pth --imagesetfile /mnt/cvpr_dataset/test/test_name.txt
```
trained_model is the weight file you want to test, our model is available at here, password is ectq

Results

Comparing with Feature Pyramid Mechanisms:

Visualization of the performance of both the baseline SSD and the MuBo-integrated model:

Citation

If this work helps your research, please cite the following paper.

@inproceedings{Tao:ICCV21,
  author    = {Renshuai Tao and Yanlu Wei and Xiangjian Jiang and Hainan Li and Haotong Qin and Jiakai Wang and Yuqing Ma and Libo Zhang and Xianglong Liu*},
  title     = {Towards Real-world X-ray Security Inspection: A High-quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection},
  booktitle = {IEEE ICCV},
  year      = {2021},
  }