This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).

The COVID-19 pandemic has accelerated the adoption of artificial intelligence, or machine learning, in 2021. The need for automation among enterprises combined with the advancements in AI hardware and software is turning applied AI into a reality. Language models are based on natural language processing techniques and algorithms to determine the probability of a given sequence of words occurring in a sentence. These models can predict the next word in a sentence, summarize textual information, and even create visual charts from plain text. Large language models (LLMs) are trained on massive datasets that contain enormous amounts of data.

Machine learning is becoming essential for a lot of companies and they want to use it to optimize their operations and make new services. One of the challenges is that sometimes you need to deploy a model in an environment where you have limited internet connection and no operators to manage the infrastructure for ML. In this case, you need to use Machine Learning on Edge and have a way to deploy and monitor your models and applications remotely. AWS Panorama is a machine learning device by AWS with a software development kit and corresponding AWS service which manages devices and applications. It is focused on working with computer vision models and video streams.

The Cincinnati/Northern Kentucky International Airport (CVG) is expanding its long-standing relationship with TaskWatch to help automate manual processes and gain insight into its complex operations. Adding TaskWatch's computer vision platform that works in conjunction with AWS Panorama, the Airport sees, adds, and explores distinct use cases for advanced artificial intelligence (AI). TaskWatch is leading the way for continuous improvement in capacity management and productivity, which are vital contributors to the overall customer experience. Brian Cobb, CIO at CVG, explains: "CVG Airport is committed to providing a world-class traveler experience through continuous innovation and strategic partnerships. Using TaskWatch's application on AWS Panorama, we can bring computer vision to our existing IP cameras to automatically monitor congestion for over 70,000 square feet of airport traffic lanes.

Amazon is rolling out cheap new tools that will allow factories everywhere to monitor their workers and machines, as the tech giant looks to boost its presence in the industrial sector. Launched by Amazon's cloud arm AWS, the new machine-learning-based services include hardware to monitor the health of heavy machinery and computer vision capable of detecting whether workers are complying with social distancing. Amazon said it had created a two-inch, low-cost sensor--Monitron--that can be attached to equipment to monitor abnormal vibrations or temperatures and predict future faults. AWS Panorama, meanwhile, is a service that uses computer vision to analyze footage gathered by cameras within facilities, automatically detecting safety and compliance issues such as workers not wearing PPE or vehicles being driven in unauthorized areas. The new services, announced on Tuesday during the company's annual cloud computing conference, represent a step up in the tech giant's efforts to gather and crunch real-world data in areas it currently feels are underserved.

Yesterday at AWS re:Invent 2020, we announced AWS Panorama, a new machine learning (ML) Appliance and SDK, which allows organizations to bring computer vision (CV) to their on-premises cameras to make automated predictions with high accuracy and low latency. In this post, you learn how customers across a range of industries are using AWS Panorama to improve their operations by automating monitoring and visual inspection tasks. For many organizations, deriving actionable insights from onsite camera video feeds to improve operations remains a challenge, whether it be increasing manufacturing quality, ensuring safety or operating compliance of their facilities, or analyzing customer traffic in retail locations. To derive these insights, customers must monitor live video of facilities or equipment, or review recorded footage after an incident has occurred, which is manual, error-prone, and difficult to scale. Customers have begun to take advantage of CV models running in the cloud to automate these visual inspection tasks, but there are circumstances when relying exclusively on the cloud isn't optimal due to latency requirements or intermittent connectivity.