Personal Protection Equipment Detection based on Deep Learning

Real time Personal Protection Equipment(PPE) detection running on NVIDIA Jetson TX2 and Ubuntu 16.04

Person, HardHat and Vest detection
Input from Video file or USB Camera
A backend service which can push message to "console" or "Cisco® Webex Teams space" when an abnormal event is detected.

Requirements

NVIDIA Jetson TX2 or Ubuntu 16.04
NVIDIA GPU on Ubuntu 16.04 is optional
Python3

How to run

Video Inference Service

$ cd inference
$ pip3 install -r requirements.txt
$ python3 video_demo.py --model_dir=xxx  --video_file_name=xxx --show_video_window=xxx --camera_id=xxx

model_dir: the path to model directory
video_file_name: input video file name or usb camera device name, you can get camera device name on ubuntu or NVIDIA Jeston by running

$ ls /dev/video*

show_video_window: the flag to show video window, the options are {0, 1}
camera_id: It is just convenient for humans to distinguish between different cameras, and you can assign any value, such as camera001

Backend Service

run the following command

$ cd backend
$ pip3 install -r requirements.txt
$ python3 main.py

run application as docker

docker-compose up
or
docker-compose up --build

send notification

By default, it will use the console notification, this just print the notification to stdout.
If you want to use Cisco® Webex Teams, use change the config referring to config.py.
Or you can write your own if you write your provider inheriting the notification.Provider

setup Cisco® Webex Teams

create a robot referring to https://developer.cisco.com/webex-teams/, you will get the token
create a webex-teams room and add the robot to that team
go to https://developer.webex.com/docs/api/v1/rooms/list-rooms to get the new created room id
put the above info to the config.py

Alert Message Format

total_person: number of people detected
without_hardhat: number of people without hard hat
without_vest: number of people without Vest
without_both: number of people without hard hat and vest

Training Program

Based on TensorFlow Object Detection API, using pretrained ssd_mobilenet_v1 on COCO dataset to initialize weights.

Training Data

coming soon!

Reference work

TensorFlow Object Detection: https://github.com/tensorflow/models/tree/master/research/object_detection