How our PPE detection model help healthcare industry resist infections – Overview
- A newly designed computer called PPE detection automates tracking and monitoring the durability of PPE equipment.
- To shield your employees from work hazards, PPE detection is crucial. Machine intelligence can automate the detection system, saving you money and time and developing a responsive worker protection culture.
- Every hospital’s PPE inspections is automated using AI-powered PPE detection. It can identify employees’ PPE with still images or real-time video. Safety managers can set rules for notifications if a worker is not implementing the norms and when they enter a work area without PPE because such detection is instantaneous and in real-time.
- Personal Protection equipment Equipment (PPE) is one of the preventative measures to protect workers from their hazardous and dangerous work environment, whether it’s in construction, healthcare, or the chemical sector.
- PPE detection systems can be used on job sites to increase user safety, prevent mishaps, and detect and record safety breaches and hazards of work injuries more effectively.
- PPE must still be manually checked by dedicated humans, which not only requires money but also creates margin for error.
Application of PPE detection in healthcare:
- Maintaining company and customer safety and reducing any dangers to your staff members’ initial and long health depend on it.
- Wearing personal protection gear also helps to stop the transmission of diseases and germs by one person to another. For instance, dentists must wear protective equipment to avoid contamination from getting on their instruments. Carpet cleaners must prevent cross-contamination and the spread of infection out across business..
- Personal protection equipment, or PPE, helps to prevent staff emergencies at the job that are produced by inhalation, absorption, irritants, or other prolonged contact with a disinfectant.
- This actively security measures, promotes worker health, and makes the workplace safe and secure.
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.