Occupational Safety with Computer Vision
Executive Summary
The client is a Brazilian company in the paper and packaging sector, recognized for its sustainable solutions and the integration of the entire production chain — from forest cultivation to the conversion of paper into packaging. With a focus on innovation, quality, and responsible environmental management, the company sought to also elevate the standard of safety in its industrial environments.
The occupational safety scenario required evolution: monitoring was conducted manually, with teams dedicated to observing cameras in real-time. This model limited the ability to identify risks continuously and proactively, leaving gaps difficult to cover with isolated human effort.
The response was the implementation of Smart Risk Prevention, a Squadra solution that combines computer vision and machine learning to detect operational risks in real-time. The result was a safer work environment, with continuous monitoring, immediate response to risk situations, and intelligence generation for preventive actions.
The Challenge
Industrial operations demand constant attention to safety protocols — and the volume of variables to monitor simultaneously made the manual model insufficient to ensure full coverage. The company identified the need for a more robust approach, capable of acting preventively and continuously.
Limited coverage monitoring: Safety management depended on people manually observing cameras, which restricted the ability to capture the entirety of real-time risks and act before incidents occurred.
Absence of automated detection of PPE and risky behaviors: Without an automated system, identifying incorrect use or absence of personal protective equipment and inappropriate behaviors was subject to human attention and availability.
Access control to restricted areas: Continuously verifying the presence of unauthorized persons in risk zones was a significant operational challenge, with a direct impact on safety and process compliance.
The Solution
Squadra implemented Smart Risk Prevention, a solution that applies computer vision and machine learning directly to the existing camera infrastructure. The approach was structured in two phases: diagnosis and modeling of the risk environment, followed by the activation of intelligent monitoring in production.
Genius in action: Computer Vision serving industrial safety
Diagnosis and Modeling — Understanding the environment to train the AI
The first phase involved mapping the risk scenarios present in the operation and defining the parameters that the AI model should recognize. This work guided the training of computer vision algorithms to identify specific situations in the client's industrial environment.
Mapping of operational risks: Identification of critical behaviors and situations to be monitored — use of PPE, access to restricted areas, and risky behaviors.
Machine learning model training: Configuration and adjustment of algorithms based on visual patterns relevant to the client's production environment.
Implementation and Monitoring — AI in continuous operation
With the model calibrated, the solution was activated on the existing camera infrastructure, eliminating the need to replace it with specialized equipment. The system began to operate autonomously and continuously, generating alerts and data to support safety management.
Real-time detection: Immediate identification of absence or incorrect use of PPE, dangerous behaviors, and unauthorized presence in restricted areas.
Generation of preventive insights: Recording and analysis of risk patterns to guide corrective actions and continuously improve safety processes.
Results
Immediate risk detection: The system began to identify in real-time the incorrect use or absence of PPE and dangerous behaviors, replacing manual monitoring with continuous, gap-free coverage.
Automated access control: Verification of authorized persons in restricted areas became automatic, reducing reliance on human supervision to ensure compliance.
Preventive intelligence: The solution generated risk pattern analysis capabilities, providing input for preventive actions and continuous improvement of safety processes.
Leveraging existing infrastructure: Applying AI to already installed cameras made the acquisition of specialized equipment unnecessary, optimizing investment.
Safer environment: The reduction of accidents and downtimes resulted in a more stable operation and better working conditions for employees.
Gallery
Want results like these?
Talk to our experts and find out how AI can transform your business.
