Machine Vision and IoT
Computer vision and connected sensors that turn video, images, and signals into automated decisions.
Machine vision uses computer vision and machine learning to interpret images and video in real time — detecting objects, defects or safety violations without a person watching a screen. IoT sensors capture continuous data from the physical environment — temperature, vibration, pressure, location — that vision alone can't see. The underlying models are increasingly multimodal, so the same system that detects a defect can also reason about it and generate a written report. Combined, they give a real-time, automated view of physical operations. Avocado builds these solutions for safety compliance, quality control and predictive maintenance.
You'll recognise the situation
You manage a large physical estate — a port, airport, car park, fleet — and want video and IoT data working together.
Safety compliance (PPE, hazard zones) relies on manual spot-checks rather than continuous monitoring.
Quality control on a production line depends on human inspection, with inconsistent results and missed defects.
Equipment failures and downtime are discovered after the fact rather than predicted in advance.
You have existing CCTV or sensor infrastructure and want more value from it rather than replacing it.
From PPE monitoring to predictive maintenance
PPE & safety compliance
Real-time monitoring of required safety gear with automated alerts the moment a violation is detected, and compliance reporting for audits. Rules per zone.
Defect detection
High-resolution imaging and ML trained to detect cracks, defects and anomalies on production lines and infrastructure — GPS-tagged and compared over time.
Equipment & obstruction detection
Detecting people, vehicles and obstructions in equipment traffic zones to prevent collisions and injury.
Counting & classification
Counting and classifying vehicles in real time, number-plate recognition and materials classification to automate weighbridge ticketing.
Predictive maintenance
Fusing IoT sensor data (temperature, vibration, performance) with video to detect early warning signs of failure before downtime happens.
Smart environments & autonomous inspection
Fusing video and IoT across a large estate, plus drone, robotic and humanoid inspection for hazardous or hard-to-reach environments.
Scope, train, integrate, refine
Scope
Identify the use case, the cameras or sensors available (or needed), and the specific events or defects to detect.
Train
Train vision models on representative footage from your actual environment, not generic stock data.
Integrate
Connect models to edge or cloud infrastructure so detection happens in real time, including low-connectivity environments.
Deploy & refine
Roll out with automated alerts and reporting, then refine the model against false positives and new scenarios.
The stack behind it
Computer vision models using neural networks and bounding-box object detection; edge computing devices for low-latency, low-connectivity processing; cloud aggregation on Azure, AWS or GCP for fleet-wide monitoring and model improvement over time.
What you walk away with
Scoped individually based on the environment, existing camera/sensor infrastructure and the specific events to detect. Contact us for a tailored quote.
Common questions
What is machine vision?
Computer vision and machine learning interpreting images and video in real time — detecting objects, defects or safety violations automatically, without a person watching continuously.
Can this use our existing CCTV?
In most cases existing CCTV and camera infrastructure can be integrated directly — it doesn't require replacing what you already have.
How accurate is detection?
Accuracy depends on the quality and volume of training footage from your actual environment — which is why the training phase uses your real images and video, not stock data.
Can it work with poor connectivity?
Yes — detection can run on edge devices on-site, with results sent to the cloud when connectivity allows, rather than depending on a constant connection.
How does this connect to predictive maintenance?
By fusing video and image data with IoT sensor data (temperature, vibration, performance), the system identifies early warning signs of failure before a breakdown.
Ready to see more, automatically?
Tell us the safety, quality or maintenance problem you're trying to solve.