Ribosome-rbx / Realtime-Medical-Mask-Detection-Based-on-Faster-RCNN

Based on Faster R-CNN, we train model on our mask dataset and leverage data augmentation to preprocess our data. Mean average precision is introduced to evaluate the model performance. You'll see data augmentation and mAP evaluation in detailed explainations, and tutorials of faster-rcnn training

Real-time Medical Mask Detection Based on Faster RCNN

This is an official implementation of blog: website-under-construction

Dataset

We use a small dataset Mask Detection, found available on Kaggle. In this dataset, we have 853 images belonging to 3 classes ('with mask', 'without mask' and 'masks worn incorrectly'). And the whole dataset is stored in data_augmentation/input.

Data Augmentation

With files(images and annotations) to augment put in input/(in two sub folders). Create a output folder under directory of '../data_augmentation' with two sub-folders images and annotations in it. The relative locations of files are as follows：

|--data_augmentation
   |--util
   |--augment.py
   |--input
   |--output
      |--images
      |--annotations      

Change the INPUT_DIR and OUTPUT_DIR in augment.py to corresponding paths. AUGMENT_SIZE decides how many augmented images will be produced from one original image.

INPUT_DIR = '../data_augmentation/input'
OUTPUT_DIR = '../data_augmentation/output'
AUGMENT_SIZE = 4

Run script:

python ../data_augmentation/augment.py

Augmented images and annotations are generated in output/.

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

pip install -r requirements.txt

Train

Open mask-fasterrcnn.ipynb with Colab. Change data_path and output_path in augment.py to corresponding paths, and run blocks in the notebook.

data_path = r'../data_augmentation/output'
output_path = r'../results'

Real-time Mask Detection

Since colab cannot connect to the local camera on your computer, you have to build a local environment. After that, change model_path to corresponding local path:

model_path = r'C:\Downloads\Epoch_24_model.pt'

Run script:

python C:\Downloads\Medical-Mask-Detection-Based-on-Faster-RCNN\realtime\camera.py

However, the processing speed is much slower than our expectation, which only achieves to detect one frame for each five seconds. The way for feeding frame into the model have a lot of room to be optimized. For more details, please check the source code.

About me

EDUCATION:

• Bachelor of Communication Engineering (2022.7 Expected)
• Qiushi Honor College, Tianjin University (TJU)

CONTACT ME:

• E-mail: [e-mail]
• Address: 92 Weijin Road, Tianjin, P.R. China