Our Case StudiesSecuring Revenue and Fleet Efficiency with Computer Vision.
Softlabs Group developed an AI-powered fleet tracking system to eliminate manual blind spots in quarry operations. Using advanced object detection and ANPR, the solution automates load verification and entry tracking, ensuring 100% visibility and preventing unauthorized material extraction.
Mining & Quarrying
Enterprise AI Vision System
Agile Development
Edge AI & Web Dashboard
We partnered with a leading Australian mining operator, managing vast open-pit quarries in Western Australia.
They needed a robust, automated solution to track fleet movements, verify load status to prevent theft and enforce strict vehicle safety protocols across their remote extraction sites.
Australia
Heavy vehicle interactions are a primary cause of industrial accidents. With the mining sector reporting frequent high-potential incidents, automated monitoring is essential to enforce exclusion zones and prevent collisions.
In quarrying, "cycle time" is money. Manual tracking creates data gaps, whereas optimizing the load-haul-dump cycle can significantly boost profitability by eliminating micro-delays that compound over thousands of trips.
Unauthorized material transport is a major revenue leak. Strict monitoring is required to prevent illegal vehicle use and unrecorded load extraction, ensuring every gram of material is accounted for.
We developed a Computer Vision-based Fleet Tracking System that sits as an intelligent layer over existing IP surveillance infrastructure (e.g., Bosch cameras). The solution uses advanced Object Detection to monitor entry/exit points and weighbridges 24/7.
Unlike simple motion sensors, our AI distinguishes between trucks and trailers, reads license plates (ANPR) for authentication and visually verifies whether a truck is "Filled" or "Empty" in real-time. This data is instantly pushed to a central logistics dashboard, giving site managers total visibility without manual logging.
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Explore How it Works
Gatekeepers struggled to manually record plates during peak hours, causing queues.
Disagreements on whether trucks arrived/left full or empty affected billing.
Unregistered vehicles entering effectively "stole" loads without record.
Lack of data made it hard to optimize truck turnaround times.
Sites were in remote areas with limited bandwidth for cloud uploads.
AI cameras capture and digitize license plates instantly at high speeds.
Deployed edge AI devices for offline processing
System cross-references plates with the approved whitelist and triggers alarms for unknowns.
Precise entry/exit logs allow granular analysis of fleet cycle efficiency.
Deployed AI models locally on edge devices to process video on-site, syncing only text data.
Eliminated manual blind spots, providing a verifiable digital audit trail for every single vehicle movement on-site.
Drastically reduced material theft and billing errors by automating the "Full-in / Empty-out" (or vice versa) verification process.
Prevented unauthorized access by instantly flagging unknown vehicles, ensuring compliance with strict mining site safety regulations.
Reduced gate congestion and wait times, allowing for faster truck turnaround and higher daily material throughput.
Distinguishes between the cab and the trailer to ensure accurate unit counting.
Uses deep learning to visually analyze the truck bed and determine if it is carrying material.
High-accuracy text extraction from plates, optimized for dusty/muddy mining conditions.
A lightweight web interface for live monitoring, historical search, and automated reporting.
Material delivery tracking and site access control.
Dump truck monitoring and landfill entry tracking.
Trailer yard management and gate throughput optimization.
Container truck throughput and terminal security.
Python
YOLO
ResNet
RTSP
ONVIF
Node.js
.NET Core
PostgreSQL
Edge AI Devices
Cloud Sync
Years of Experience
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
No. Our solution is primarily Computer Vision-based. We leverage cameras placed at key checkpoints (weighbridges, entry/exit gates, crushers). This means we track the fleet externally without needing expensive GPS hardware installed inside every vehicle, making deployment faster and non-intrusive.
Yes, The system is built on Edge Computing architecture. The video processing happens locally on the device at the site. It only sends text-based metadata (e.g., "Truck A entered at 10:00 AM") to the cloud, which requires very little bandwidth. If the network fails completely, the data syncs automatically once connectivity is restored.
Yes. The AI vision models are trained to classify load levels. It can distinguish between "Empty," "Half-Filled," and "Full" trucks. If a truck leaves the site "Full" but arrives at the crusher "Half-Filled," the system flags it instantly for potential theft or spillage.
Absolutely. We can integrate via API or database connectors to match the visual data (AI sees a truck) with the weight data (Weighbridge records 20 tons). This creates a "Double-Check" audit trail that eliminates billing fraud.
