Our Case StudiesEnsuring Quarry Vehicle Compliance with AI-Based Load Cover Detection
This AI solution developed by Softlabs Group automates safe material transport checks using computer vision. It detects whether heavy vehicles exiting construction and quarry sites have their tarpaulin covers in place—ensuring safety, compliance and environmental protection.
Supply Chain & Manufacturing
Enterprise
Agile Scrum
.Net Application, Server
FP McCann is one of the UK's leading manufacturers in the construction industry, committed to high-quality solutions across various sectors such as buildings, drainage, and civil engineering. With a reputation for innovation and excellence, they have embraced our AI solution like "Cover Loads" to enhance compliance and safety in material transportation.
This move signifies their dedication to leveraging cutting-edge technology for improving operational efficiency and adhering to environmental and safety standards in their projects.
UK
Quarry operators and heavy-material transporters face increasing pressure to meet safety and environmental regulations.
According to a Deloitte study on AI in logistics, AI can reduce transport-related safety violations by up to 25% through automated inspection systems. Source - Deloitte AI in Logistics Report
Traditional exit inspections rely on human guards, who may miss uncovered trucks during peak hours or bad weather-resulting in regulatory fines or environmental hazards.
To close this safety gap, FP McCann partnered with Softlabs Group to develop an AI solution that could detect tarpaulin covers on moving vehicles-fully automated and real-time.
As their AI development partner, we built Cover Loads, an edge-deployed AI vision system that detects whether trucks are properly covered as they exit quarry sites.
The system uses YOLOv4-tiny object detection to identify the presence (or absence) of tarp covers and integrates with boom barrier systems to restrict unauthorized exits. A lightweight dashboard allows supervisors to review logs, alerts and flagged footage.
The AI model was optimized to handle various truck shapes, angles, and environmental conditions and deployed on-site using edge devices for real-time decisioning with no cloud dependency.
A truck carrying construction material approaches the quarry exit, triggering the system to initiate compliance verification.
Strategically placed cameras snap high-resolution images of the vehicle, feeding data into the AI-driven Cover Loads system.
YOLOv7, a powerful deep learning model, processes the images to detect the truck and check for tarp coverage on the load.
The system references a material-specific database to determine if that particular load requires full tarp coverage.
If a tarp is detected, the AI examines its fit and coverage quality, ensuring it meets transport safety and environmental standards.
In case of non-compliance, alerts with time-stamped images are sent instantly to quarry managers and safety officers.
This automated process ensures only compliant trucks leave the premises, reducing roadside risks and regulatory violations.
Manual inspections were error-prone and inconsistent
No scalable system to validate cover compliance
High cost of missed violations (fines, hazards)
Required real-time, zero-latency detection
Needed to integrate with physical gates and access controls
Used YOLOv4-tiny for real-time tarp detection
Deployed edge AI devices for offline processing
Connected system to boom barriers for automatic exit control
Built a visual alert dashboard for supervisors
Calibrated model for multiple truck types and angles
Model achieved reliable performance in varied weather and lighting conditions.
Zero manual effort needed at vehicle exits after deployment.
System flagged all uncovered trucks, minimizing safety violations.
Solution rolled out to live quarry site in under 2 weeks.
Solution can be scaled across FP McCann’s other facilities with minor calibration.
A solution originally built for transport safety is now adaptable across industries:
Ensure vehicle compliance inside manufacturing units
Monitor haul truck load safety in mines
Gate check for safety gear and cargo seals
Detect load security in real-time
Automated visual checks for loaded containers
Preventing collisions by detecting humans and obstacles in loading zones.
Tracking real-time movement of goods, pallets and material trolleys.
Ensuring safety compliance through activity recognition and alerts.
Monitoring material flow across supply chain and dispatch routes.
Identifying risky behaviors or routes in industrial environments.
React JS
.Net
msSQL
YOLOv4-tiny
OpenCV
ONNX Runtime
Boom barriers
CCTV
Local Alarms
Years of Experienced
Countries
Clients
Projects
At Softlabs Group, we take pride in solving complex business challenges with innovative and reliable solutions. Our case studies showcase how we’ve empowered clients across industries with tailored software that delivers measurable results and drives success.
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FAQs
Yes, the AI uses computer vision to detect humans in real-time and trigger safety alerts.
Absolutely, it can be integrated with both manned and unmanned vehicles for enhanced operational safety.
Yes, the system is trained to function in challenging industrial conditions using high-accuracy models.
Yes, the solution flags risky interactions and alerts operators or systems before impact.
Deployment typically takes 2–3 weeks depending on the site layout and integration scope.
Yes, it is designed to plug into existing camera feeds and safety control units with minimal disruption.
Yes, live dashboards and alerts are accessible across desktop and mobile devices.
No, the system supports edge processing and can operate offline with periodic syncing.
The system can log the event, trigger alerts and optionally halt operations via connected protocols.
Yes, the architecture supports multi-zone setups and can scale across sites or units.
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IT Manager, FP McCann Ltd. - UK
Operations Manager, Symbio Energy Limited. - UK
CEO, Wiseman Talent Ventures - Kenya
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