Why are neural networks becoming a key tool for industrial safety?
Safety in manufacturing has become not just a regulatory requirement, but a key factor in business resilience. Modern enterprises operate with high loads, complex infrastructure, and the human factor, which remains the main source of accidents and downtime. Against this backdrop, neural networks are becoming the tool that allows predicting dangerous situations, identifying deviations, and preventing incidents long before they cause damage.
AI transforms production data into an early warning system: it analyzes video streams, telemetry, equipment logs, employee behavior, and external conditions. Instead of a reactive approach, enterprises are moving to a predictive safety model—where risks are eliminated before they become a threat.
Key areas of application of neural networks in production safety
AI operates at several levels of the enterprise, improving both technical and operational safety.
1. Computer vision and real-time monitoring
- PPE Monitoring: models detect the absence of hard hats, gloves, glasses, or work clothes.
- Hazard zone detection: AI tracks entry into high-risk zones where only trained personnel are allowed.
- Violation detection: running, aggressive movements, falls, violation of technological instructions.
2. Predictive equipment analytics
Neural networks analyze vibration, temperature, load, and electrical parameters, predicting the probability of component failure. This helps minimize accidents related to wear or improper operation of machines.
3. Employee behavior analysis
Models detect deviations in personnel actions, fatigue, decreased concentration—factors that most often lead to errors. This is especially important in enterprises with a high repeatability of operations.
If a company is just starting to assess the possibility of implementing AI, it is convenient to preliminarily estimate the budget
→ use the cost calculator for neural network development from EasyByte.
And if you need to select a specific architecture and approach, you can
→ register for a free consultation with an EasyByte expert.
Real-world cases of using neural networks to improve safety in production
Case #1: Ignitis Group—PPE monitoring and reduction of incidents at a thermal power plant
→ Ignitis Group implemented an EasyFlow computer vision system for automatic PPE monitoring at the Kaunas CHP. The real-time solution tracks compliance with rules (hard hats, vests, etc.), records violations with photos and immediately sends notifications to occupational safety specialists. As a result, the company received stricter safety control, a reduction in violations, and measurable savings due to a reduction in accidents and downtime.
Case #2: VIA Technologies—AI-powered PPE Inspection Solution for industrial and construction sites
→ VIA has developed an AI-powered PPE Inspection Solution that automatically checks whether employees are wearing hard hats, vests, and other PPE when entering hazardous areas using computer vision and Edge AI. The system reduces the workload on occupational safety services, reduces manual checks at checkpoints, and improves compliance with safety regulations at production and construction sites.
How do enterprises scale AI safety?
After initial pilots, companies quickly expand the use of AI to related processes. Most often—to quality control, internal logistics within workshops, microclimate management, work schedule optimization, and employee training. Neural networks become part of the enterprise's operating system: the more data they receive, the more accurately they predict risks and prevent dangerous scenarios.
For businesses, this means not just a reduction in incidents, but long-term efficiency gains—from fewer downtimes to increased trust from employees and auditors.
📌FAQ: frequently asked questions about using neural networks in production to improve safety
Question: What data is needed to launch an AI safety system?
Answer: Usually—video streams, equipment sensor data, incident logs, and personnel movement information.
Question: Can AI completely replace occupational safety services?
Answer: No. Neural networks enhance their work, but specialists make the final decisions.
Question: How accurate are neural networks in recognizing violations?
Answer: Modern models achieve 90+% accuracy with high-quality data.
Question: Is it difficult to integrate AI into existing production systems?
Answer: Most solutions connect via APIs and are easily linked with video surveillance and IoT platforms.
Question: Is AI safety suitable for small manufacturing businesses?
Answer: Yes. Modern solutions scale and can work even on individual lines or workshops.