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Guidance for Vision-Based Personal Protective Equipment Identification on AWS

Overview

This Guidance demonstrates how to build a computer vision application that identifies personal protective equipment (PPE) to help you track potential violations in factories. Once a violation is detected, such as an individual not wearing a helmet, the application will automatically send an alert to the individual’s manager along with photo evidence. Compared to traditional manual inspections, this Guidance can improve maintenance precision while also helping you save on labor costs.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Amazon CloudWatch helps you understand the state of business outcomes through CloudWatch metrics and alarms that monitor performance in near real-time.

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AWS Identity and Access Management (IAM) allows you to create and manage users, groups, and roles, and define policies that control access to AWS resources. IAM manages access by implementing secure authentication and authorization mechanisms. This Guidance also uses multi-factor authentication (MFA) for additional security.

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Lambda runs code without provisioning or managing servers, making it possible to simplify scaling and reduce the risk of data corruption or loss. Amazon S3 and DynamoDB support automated backups, snapshots, and disaster recovery strategies by replicating data across multiple Regions. These services allow you to regularly back up data and to recover data after downtime.

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AWS IoT Core, Amazon S3, and DynamoDB are optimized for handling large volumes of data, while Amazon SNS allows you to easily integrate with other services and manage notifications. SageMaker enables you to build and train machine learning models at scale. We selected these services for this Guidance to support the scalability and performance requirements of the application.

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Lambda can automatically provision and de-provision resources based on demand and deploys serverless image processing functions to reduce costs. CloudWatch helps monitor resource utilization and invokes scaling actions based on predefined thresholds. CloudWatch also helps monitor data transfer and sets up alerts to notify you when you reach specific thresholds.

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Amazon S3 and DynamoDB can support data access and storage patterns. These services scale so that you won’t be affected by performance issues when traffic rises, and you won’t have to pay for unused resources when traffic falls.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.