We then deploy it at the edge with the AWS Panorama device. The following sections of this blog post outline how we use live-stream videos from one of the Tyson Foods plants to train an object detection model using Amazon SageMaker. It is also crucial for inventory management use cases, such as meat tray counting for Tyson, to reduce waste by creating a feedback loop with production processes, cost savings, and delivery of customer shipments on time. This technology is currently being used in various real-life applications such as pedestrian spotting in autonomous vehicles, detecting tumors in medical scans, people counting systems to monitor footfall in retail spaces, amongst others. Object detection is one of the most commonly used CV algorithms that can localize the position of objects in images and videos. It reduces latency to/from the cloud and bandwidth costs, while providing an easy-to-use interface for managing devices and applications at the edge. AWS Panorama removes these requirements and enables Tyson to process video streams at the edge on the AWS Panorama Appliance. Streaming and processing on-premise video streams at the cloud for CV applications requires high network bandwidth and provisioning of relevant infrastructure. This enables implementation of corrective measures and improves production efficiency. CV can be effectively used in such scenarios to accurately estimate the amount of chicken processed in real-time, empowering employees to identify potential bottlenecks in packaging and production lines as they occur. With a chicken processing capacity of 45,000,000 head per week, production accuracy and efficiency are critical to Tyson’s business. Alternate strategies such as monitoring hourly total weight of production per rack does not provide immediate feedback to the plant employees. However, current manual techniques to count chicken trays that pass QA are not accurate and do not present a clear picture of over/under production levels. In order to meet customer demand and to stay ahead of any production issue, Tyson closely monitors packed chicken tray counts. Tyson employs strict quality assurance (QA) measures in their packaging lines, ensuring that only those packaged products that pass their quality control protocols are shipped to its customers. Operational excellence is a key priority at Tyson Foods. We will focus on the data collection and labeling, training, and deploying of CV models at the edge using Amazon SageMaker, Apache MXNet Gluon, and AWS Panorama. In this post, we provide an overview of the AWS architecture and a complete walkthrough of the solution to demonstrate the key components in the tray counting pipeline set up at Tyson’s plant. Tyson collaborated with Amazon ML Solutions Lab to create a state-of-the-art chicken tray counting CV solution that provides real-time insights into packed inventory levels. In Feb 2020, Tyson announced its plan to bring Computer Vision (CV) to its chicken plants and launched a pilot with AWS to pioneer efforts on inventory management. In part two, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment.Īs one of the largest processors and marketers of chicken, beef, and pork in the world, Tyson Foods, Inc., is known for bringing innovative solutions to their production and packing plants. In part one, we discuss an inventory counting application for packaging lines built using Amazon SageMaker and AWS Panorama. This is the first in a two-part blog series on how Tyson Foods, Inc., is utilizing machine learning to automate industrial processes at their meat packing plants by bringing the benefits of artificial intelligence applications at the edge.
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