AI Video Analytics for Safer Mining Operations

Industry

Mining & Resources

Duration

6 months

Team Size

6 people

Project Overview

A large-scale mining and resources operator with safety-critical operations. The mining site required real-time monitoring solutions to ensure worker safety, compliance, and environmental hazard detection, without replacing existing infrastructure. The AI Video Analytics suite was engaged to enhance operational safety and efficiency through intelligent video analytics.

Challenges

Our client needed a real-time monitoring system that could reduce the dependency on manual patrols while allowing supervisors to maintain full authority over safety operations. The site faced several critical challenges impacting both security and worker protection

1
Perimeter Monitoring: Vast operational zones made it difficult for patrols to cover every area effectively, leading to potential blind spots where intrusions or unauthorized vehicles could go unnoticed.
2
PPE Compliance: Safety inspections were manual and inconsistent, resulting in lapses where workers entered without essential protective gear such as helmets and vests.
3
Fire & Smoke Hazards: Located in a bushfire-prone region, relied heavily on human observers, which often delayed the detection of smoke or flames during emergencies.
4
Crusher Operations Monitoring: Tracks operational status, downtime, and operator activity from the control room for improved efficiency.
5
Weighbridge Monitoring: Captures accurate truck weights and manages readings to streamline material handling and logistics.
6
Fuel Point Monitoring: Monitors fuel loading into trucks, including duration and quantity, ensuring accountability and preventing wastage.

Our Approach

How We Worked on the Project

1
Pilot Deployment: We began with a single perimeter gate to test system accuracy and workflows.
2
End-User Involvement: Safety supervisors and patrol staff participated from day one, providing feedback on alerts and control room notifications.
3
Training Program: Front-line teams were trained to interpret AI alerts, confidence scores, and override options. Supervisors co-designed the override categories to match existing reporting processes.
4
Feedback Loop: False positives and missed detections were reported to refine AI models. Retrained versions were deployed in consultation with safety staff.
5
Full Rollout: Once pilot benchmarks were achieved, the system expanded across the entire site with staff-led onboarding.

Our Comprehensive Solution

Core Features & Innovations

1
PPE Detection: Automatically detects missing helmets vests and other protective gear, ensuring consistent worker safety and compliance on-site.
2
Perimeter Intrusion Monitoring: Identifies unauthorized personnel or vehicles in restricted areas, enabling quick alerts and rapid response.
3
Fire & Smoke Detection: Detects early signs of smoke or fire, triggering alerts within seconds for immediate safety action.
4
Edge Processing: Processes video locally on-site for faster alerts, enhanced privacy, and reduced cloud dependency.
5
Human Oversight: Supervisors review, validate, or override alerts, maintaining accountability and human control over decisions.

Services Offered

Video Analytics

Real-time monitoring for PPE compliance, perimeter security, and fire or smoke detection.

Web Application:

User-friendly interface integrated with control room for easy alert management.

AI & ML:

Smart models to detect risks accurately and improve over time with feedback.

DevOps & Cloud:

Secure, scalable on-site infrastructure for fast, reliable processing and alerts.

Tech Stack

The technologies and frameworks we leveraged to build a robust, scalable, and high-performance solution.

Streamlit

Web Application Framework

Python

Programming Language

OpenCV

Computer Vision Library

Docker

Containerization

SQL

Database

Impact and Results

1
High PPE Detection Accuracy: Achieved ~94% accuracy in identifying missing helmets, vests, and other protective gear under varied conditions.
2
Rapid Fire/Smoke Alerts: Detected smoke or fire in under 10 seconds, enabling quick response and improving site safety.
3
Reduced False Positives: False alerts decreased by ~30% after retraining cycles guided by supervisor feedback, enhancing reliability.
4
Lower Patrol Workload: Automated monitoring reduced manual patrols by ~25%, allowing staff to focus on higher-value tasks.
5
Crusher Operations Monitoring: Improved oversight of crusher work, operator presence, and downtime management from the control room.
6
Weighbridge Monitoring: Ensured accurate truck weights and streamlined material handling and logistics.
7
Fuel Point Monitoring: Tracked fuel loading durations and quantities, improving accountability and reducing wastage.
8
Audit-Ready Logs: Immutable records of alerts, decisions, and overrides supported compliance and safety audits.

Conclusion

1
Katomaran Technologies’ Video Analytics Suite helped a mining operatoration improve safety, compliance, and operational efficiency. By combining AI-powered detection with human oversight, the system ensured alerts were accurate, timely, and actionable while maintaining full accountability. It reduced manual patrols, improved response to hazards, and provided audit-ready logs to support safety and compliance reviews.
2
The solution is flexible and scalable, making it transferable to other industries such as municipal councils, construction sites, and public facilities. It can be used for perimeter monitoring, PPE compliance checks, and early fire or smoke detection. With reliable performance and ethical governance built-in, Katomaran Technologies delivers a trustable, practical approach to improving safety in high-risk environments.

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