Across several industries such as manufacturing, food processing, chemical, healthcare, energy, and logistics improving workplace safety is usually a top priority. In addition, due to the COVID-19 pandemic, wearing PPE in public places has become important to reduce the spread of virus. However, even when people do their best to follow PPE guidelines, sometimes they can inadvertently forget to wear PPE or not realize it is required in the area they are present in. This puts their safety at potential risk and opens the business to possible regulatory compliance issues. With Amazon Rekognition PPE detection, customers can analyze images from their on-premises cameras across all locations to automatically detect if persons in the images are wearing the required PPEs. Using these PPE detection results, customers can trigger timely alarms or notifications to remind people to wear PPE before or during their presence in a hazardous area to help improve or maintain everyone’s safety.
In this lab, you will learn how to detect if persons in the images are wearing the required PPE such as face covers, hand covers, and head covers. To start the detection of PPEs in an image, we will be using DetectProtectiveEquipment API. In the response, you will receive a detailed analysis of an image, which includes bounding boxes and confidence scores for persons (up to 15 per image) and PPE detected, confidence scores for the body parts detected, and Boolean values and confidence scores for whether the PPE covers the corresponding body part.
To run this lab, you need to have prerequisite section completed.
Click on the 7-ppe-detection.ipynb notebook.
Follow the instruction in the notebook to understand how you can detect personal protective equipment in images.
In order to execute each cell, click on the “Run” button at the top or press “Shift+Enter”.
Make sure you use “conda_python3” kernel and execute all cells in order.