![]() With this solution, team members no longer need to spend an estimated 1 hour per shift per line inspecting product carriers, which can save the company 15,000 hours of skilled labor annually in a single facility.MEMPHIS, Tenn. Tyson Foods deployed the model at the edge on an AWS Panorama Appliance, which organizations can use to connect cameras and process multiple CV applications on multiple video streams simultaneously, so that its employees are notified right away that a product carrier needs maintenance when the model identifies anomalies. To automate the process, Tyson Foods turned to Amazon Lookout for Vision, an ML service that uses CV to spot product defects in objects at scale. Using Lookout for Vision, the company created a custom ML model to analyze images and detect anomalies, without needing ML expertise. This inspection process required attention to detail and valuable operator time. Employees previously needed to manually inspect nearly 8,000 pins per line every shift because safety issues or unplanned downtime could occur if a pin fell out of place. To improve another use case with CV powered by ML, Tyson Foods developed a solution to identify faulty plastic pins that hold product carriers in place in its poultry production facilities. ![]() With this CV solution, poultry production supervisors receive near-real-time insights into production quantity, avoiding both underproduction and overproduction during the shift. Using AWS Panorama, a collection of ML devices and a software development kit that brings CV to on-premises cameras, the company was able to deploy this model at the edge to analyze video in milliseconds. This model automatically detects and counts chicken trays on video streams from production lines as employees load them onto carts. In 2021, Tyson Foods collaborated with the Amazon Machine Learning Solutions Lab (Amazon ML Solutions Lab), which pairs an organization’s team with ML experts, to train an object detection model using Amazon SageMaker with fully managed infrastructure, tools, and workflows to build, train, and deploy ML models for any use case. ![]() Alternate strategies like monitoring the hourly total weight of production per rack don’t provide data right away, preventing team members from taking action in near real time. Due to the scale of production, manual techniques for counting chicken trays that pass quality assurance measures aren’t accurate enough. Tyson Foods has the capacity to process 40 million chickens per week, and the company relies on accurate inventory measurements in the facilities to fulfill customer orders. The company approached AWS for support with implementing CV solutions powered by ML for inventory management and product carrier failure identification. Tyson Foods successfully developed an initial CV solution to augment these manual inspection processes but knew that implementing ML would increase efficiency and decrease complexity even further. Due to the scale of production at Tyson Foods facilities, manual inspection processes can be time consuming and create bottlenecks. This technology at the Amazon Go store inspired the company’s emerging technology team to pursue similar CV solutions to address challenges and increase efficiency in its production processes. CV is a process that involves capturing, processing, and analyzing images and videos so that machines can extract meaningful, contextual information from the physical world. During this cloud migration, Tyson Foods saw how the Amazon Go store was automating the checkout and retail experiences using cameras and CV. Tyson Foods started a cloud migration from data centers to AWS in 2018. In the United States, an estimated 20 percent of the country’s chicken, beef, and pork came from Tyson Foods facilities in 2021. ![]() Tyson Foods produces beef, pork, chicken, and prepared foods in over 100 facilities worldwide. ![]()
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