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Machine Vision Automates Workplace Safety in Manufacturing

factory automation

Prioritizing workplace safety in manufacturing is top of mind for every manufacturer. But achieving it can be a difficult and costly process. Reducing risk in a factory means constant monitoring to identify environmental safety issues and proper workplace precautions. And this kind of vigilance can be labor-intensive.

“On average, a 10,000-square-meter factory has to have a minimum of 10 safety personnel, with two HSE (Health, Safety, and Environment) officers required for video supervision and monitoring,” says Stephen Li, CEO at Aotu, an AI company providing machine vision-based health and safety solutions.

That represents a significant investment for manufacturers, especially as industrial facilities need round-the-clock supervision. In addition, manual monitoring comes with its own limitations.

“Plant safety personnel work hard—but they’re stretched thin. They can’t detect most health and safety problems immediately, and they’re never going to cover 100% of the scenarios,” says Li. “Plus, there are delays in responding to issues in a timely fashion, since a human being has to actually make a phone call or go to the site of a safety violation in person to observe or correct it.”

It’s a challenging situation for manufacturers, who are committed to worker safety but are also under pressure to tighten budgets and optimize processes. But new machine vision health and safety solutions may provide an answer that keeps factory workers safe and satisfies the demand for greater efficiency.

Machine Vision Automates Safety Monitoring in Bottling Plant

Deployment at a bottling plant in China based on Aotu’s machine vision solution is a case in point.

The plant is operated by a major beverage company. The sheer size of the facility means many different areas to monitor, including rooftops, ceilings, boilers, waste areas, warehousing facilities, and more. Plus, the bustling site is filled with factory workers performing a wide variety of tasks, making supervision of employee behavior a complex undertaking.

In collaboration with Intel, Aotu developed a machine vision-based health and safety solution designed to analyze video feeds from the bottling plant and automatically alert safety personnel when an issue is detected.

“AI can monitor workplace environments in real time, identifying potential hazards and ensuring compliance with safety protocols. This proactive approach to safety can reduce accidents and improve factory workers’ well-being,” says Zhuo Wu, Software Architect at Intel.

The system’s AI algorithms are configured to monitor for environmental safety issues. The deployments cover nearly 1,000 key supervision points within the factories. At the same time, AI also analyzes video feeds for behavior-based safety violations: failure to wear proper protective gear, unsafe climbing and walking, unauthorized access to high-risk areas, violations of maximum occupancy limits, and so on.

If the system detects a problem, it captures a 30-second recording of the safety issue, classifies it as either a major or minor emergency, and sends an alert to a human supervisor for verification and response. If the problem is serious enough, a safety official can remotely trigger an on-site alarm and warning message to alert workers to imminent danger. For less severe issues, safety personnel have the option to follow up later for resolution and worker training.

After implementing the solution, the bottling plant saw an increase in both the efficiency and the efficacy of its safety program. “The use of AI reduced the workload of the plant HSE staff, and it ensured that safety issues were no longer going to be ignored,” says Li. “In addition, safety awareness among front-line workers improved significantly.”

#MachineVision health and safety solutions are gaining traction among large manufacturers—and their adaptability, cost-effectiveness, and ease of deployment should make them attractive to SIs and smaller industrial businesses. Aotu via @insightdottech

Flexible Platform for Video Analytics

For a machine vision solution to be broadly useful to the manufacturing sector, it must be adaptable. A bottling plant, after all, is quite different from an auto parts factory, a high-tech fabrication site, or a chemical facility.

To create a robust yet flexible machine vision platform for industrial health and safety, Aotu decided to partner with Intel. Together, the companies were able to leverage the capabilities of a number of Intel® hardware and software tools:

  • 11th Generation Intel® Core processors offer optimization and acceleration for deep learning, AI, and machine vision scenarios.
  • Intel® Iris® XeGPUs are particularly well-suited to computer vision tasks such as smart video processing.
  • Intel® Xeon® scalable processors enable configurations that require heavier workloads and are also suitable for use in harsher industrial settings due to their ruggedized design and wide operating temperature range.
  • The Intel® OpenVINO toolkit provides pre-trained AI inferencing models and reference models for common industrial safety scenarios—as well as a foundation for the rapid development of custom AI algorithms.

The use of OpenVINO was particularly important when working with AI models for workplace safety in manufacturing scenarios. Acquiring a diverse data set that covers a variety of safety situations often requires extensive efforts—especially when it involves real-world scenarios—and developing these training models can be computationally intensive and time-consuming. Aotu has a set of tools designed to streamline the process of data collection and labeling, and with OpenVINO integration can run optimized pre-trained models, greatly speeding up the data set generation process.

“OpenVINO provides a set of tools and optimizations to enhance the performance of AI models. We use it to reduce the model size and improve inference speed without significant loss in accuracy,” says Li.

Thanks to Intel’s hardware and software capabilities, the company can offer no-code and low-code AI customization and deployment. This enables end users to execute inference tasks across different devices efficiently, maximizing computing power while achieving low latency and high throughput for their solutions.

Towards Safer and More Efficient Industry

Machine vision health and safety solutions are gaining traction among large manufacturers—and their adaptability, cost-effectiveness, and ease of deployment should make them attractive to systems integrators and smaller industrial businesses as well. Especially as more industrial environments start to implement automated solutions such as collaborative robots, industrial AI can be used to ensure AI-driven robots can work alongside humans, reducing sickness and injuries.

But beyond the health and safety benefits, the inherent flexibility of these solutions combined with the power of OpenVINO will open other use cases as well. For instance, the platform can be extended to include defect detection, production line automation, predictive maintenance, and supply chain management.

OpenVINO’s “ability to quickly process and analyze visual data makes it an invaluable tool for enhancing quality control, reducing downtime, and increasing efficiency,” Wu says.

In the future, look for computer vision to further the digital transformation of manufacturing in new and innovative ways, making Industry 4.0 safer, more efficient, and more profitable for all.

This article was edited by Christina Cardoza, Editorial Director for

The article was originally published on March 24, 2023.