How our PPE detection model help healthcare industry resist infections – Overview
- PPE detection is an emerging compute that automates the tracking and monitoring of PPE equipment wearability.
- PPE detection is essential for protecting your employees from workplace hazards. The detection system can be automated by artificial intelligence, saving you time and money, and promoting an adaptive workplace safety culture.
- AI-powered PPE detection automates PPE inspection in any facility. It can recognise the PPE of employees in pictures or live video. Because such detection is automatic and in real-time, safety managers can set rules for notifications if a worker is not abiding by the rules or when they enter a work area without PPE.
- Whether in construction, healthcare, or the chemical industry, Personal Protective Equipment (PPE) is one of the prevention measures to safeguard personnel from their hazardous and dangerous work environment.
- The use of PPE detection systems in the job sites provides improved user safety, avoiding incidents, and better detection and recording of safety violations, and risks of work injuries.
- Manual checking of PPE requires dedicated human involvement which not only implies a cost but also opens the door to human error.
Application of PPE detection in healthcare:
- It is essential for maintaining employee and customer safety, along with minimising any risks to the short- and long-term health of your staff.
- Wearing personal protection equipment also aids in preventing the spread of germs and infections from one person to another. For instance, in order to prevent bacteria from getting on their instruments, dentists must wear safety gear. Workers in the cleaning industry must avoid cross-contamination and the transmission of illness to other areas of the facility.
- Personal protective equipment, or PPE, aids in preventing workplace emergencies among staff members brought on by inhalation, absorption, irritants, or other extended contact with a cleaning chemical.
- This actively lowers accidents, enhances employee health, and creates a more safe and secure working environment.
How we helped healthcare industry by Computer Vision Model to Detect PPE Violations?
- Workplace injuries are a big source of avoidable personal loss and financial loss.
- Safety protocols like PPE requirements are routinely violated and traditional measures like safety training, manual monitoring, and safety bill-boards aren’t very effective
- Machine Learning models on TensorFlow or Yolo can be trained using annotated data using ‘Transfer Learning’.
- These models can be optimized using Hardware accelerators like Intel OpenViNo or Nvidia TensorRT to be able to run in low cost, low power SBCs (Single Board Computers)
- These models can be hosted in the Cloud (AWS, Azure, or Google) to run on video stream from IP cameras to scale easily across any number of regular CCTV cameras.
Market Size: PPE Detection.
The PPE detection market was valued at $31,282.1 thousand in 2019, and is expected to reach $ 1,979,851.9 thousand by 2027, registering a CAGR of 78.9% from 2020 to 2027.