ArcelorMittal: AI-Enhanced Railway Transportation

Critical railway car data is recognized and logged in near-real time with Intel® Distribution of OpenVINO™ toolkit.

At a Glance:

  • ArcelorMittal Poland is the largest steel producer in Poland and part of the ArcelorMittal group, the largest steel producer in the world.

  • ArcelorMittal and the Intel® Distribution of OpenVINO™ toolkit enable machine learning to identify critical data about railway cars and liberate employees from time-consuming and costly tasks.

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Executive Summary

The demand for greater efficiency is a key driver of Industry 4.0—where analytics, artificial intelligence, and the Internet of Things (IoT) are transforming decision-making and productivity. A study of senior factory executives found that 86 percent reported major increases in shop-floor data collection over the past two years, and two-thirds reported that data insights have led to quality and efficiency savings of 10 percent or more.1 A study conducted by Deloitte found that nearly all survey respondents—94 percent—reported that digital transformation is a top strategic objective for their organization.2

ArcelorMittal, a major European steel producer, is improving efficiency and streamlining operations with machine learning technologies that are designed to accurately identify key data about railway cars that transport raw materials and ready products across factories and to the end customer. These valuable advancements are liberating employees from time-consuming and costly tasks.

Challenges

ArcelorMittal Poland moves large volumes of materials—with even smaller cargo weighing 20 or more tons. These materials, which are transported by railway cars, must be tracked along their journey. During a single day, there are over a thousand cars in motion at one factory. For each car, a variety of data points must be tracked, such as material location, the condition of the materials, and how fast shipments are moving.

The company needed an automated solution that would liberate employees from needing to watch video feeds and input critical pieces of data. Not only was a scalable solution required, but also a solution that was durable enough to withstand harsh environmental conditions such as heat, rain, and cold temperatures.

Solution

ArcelorMittal Poland deployed a solution that leverages computer vision, deep learning, and near-real-time processing. The solution focuses on the identification and recognition of railway cars throughout the factory.

The camera operators previously used video technology to categorize and tag incoming cars, which was a time-consuming and tedious process. For example, there are several checkpoints located throughout the site where railway cars are weighed. Operators must be available 24/7 to tag the cars, which adds up to a large number of work hours. Using the new technology frees up operators to focus on other important tasks.

The new solution, which includes two cameras per checkpoint, has the capacity to log critical pieces of data quickly and accurately. For example, the railway cars pass through the weighing system and the algorithm, which uses the Intel® Distribution of OpenVINO™ toolkit, matching the cargo weight to the image of the car.

Figure 1: Solution schematic.

Solution Benefits Include:

  • Simplified solution that enables computer vision routines across the business
  • Ability to deliver a multiplatform solution and scale up directly in IT-managed data centers 
  • Greater performance and accuracy achieved through the Intel Distribution of OpenVINO toolkit, which allows developers and data scientists to accelerate and streamline the development of AI and deep learning applications and algorithms

ArcelorMittal Poland used the Intel Distribution of OpenVINO toolkit to run inference and deep learning models accelerated by Intel® FPGAs to realize greater efficiency.

Using GPUs was a consideration, but the company already had Intel® processors running its on-premise servers and could cost-effectively add in Intel® vision accelerators with Intel® Arria® FPGAs and the Intel Distribution of OpenVINO toolkit to accelerate inference. This allowed the company to keep the solution in IT-managed data centers, avoiding costly new infrastructure for development.

The Intel Distribution of OpenVINO toolkit has improved performance and allows the company to process up to 19 frames per second, compared to only two to three frames per second without the optimizations of the toolkit. What's more, the solution takes far less memory and is running with less than 6 GB of memory, compared with 60 to 70 GB previously.

Figure 2: The Intel® Distribution of OpenVINO™ toolkit is a free software kit that helps developers and data scientists speed up computer vision workloads and streamline deep learning deployments from the network edge to the cloud.

Conclusion

ArcelorMittal Poland is pleased with the early results of the project as the company captures valuable insights and improves accuracy. By automating important processes, ArcelorMittal Poland can improve its ROI and is positioned to compete in the next generation of industry.

The Intel Distribution of OpenVINO toolkit has evolved since the start of our journey. Now there are far more demos available that help you scale and build, and the documentation keeps getting better." —Gregorio Ferreira, data scientist and Industry 4.0 specialist

About ArcelorMittal Poland

ArcelorMittal Poland is the largest steel producer in Poland and part of the ArcelorMittal group, the largest steel producer in the world. It concentrates about 70 percent of the Polish steel industry's production capacity. The company is also one of the largest Polish exporters and producers of coke in Europe and in the entire ArcelorMittal group.

Make Your Vision a Reality on Intel® Platforms

Develop applications and solutions that emulate human vision with the Intel Distribution of OpenVINO toolkit. The toolkit extends workloads across Intel® hardware to maximize performance:

  • Enables deep learning inference at the edge.
  • Supports execution across a variety of computer vision accelerators, including CPU, GPU, VPU, Intel® Neural Compute Stick 2, and FPGA, using a common application programming interface.
  • Speeds up time to market via a library of functions and preoptimized kernels.

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