We leverage state-of-the-art neural networks — from multi-class object detection to pixel-level segmentation — to automate quality control, monitor critical infrastructure, and detect anomalies before they become failures.
Autonomous systems that help run industry and society begin with perception. Before a system can act, it has to see what is wrong — reliably, at speed, and in conditions that defeat a human inspector. That is the work below.
Real-time monitoring for fast-moving production lines using YOLO-family object detection. We detect foreign objects on heavy-duty conveyor belts, identify longitudinal tears before catastrophic failure, and spot packaging defects in low-light or dusty environments.
We convert visual data from dashcams, drones and fixed sensors into actionable maintenance schedules. Semantic segmentation with U-Nets and SAM maps paint peel on aircraft, classifies biofouling on ship hulls, and segments street-level defects like potholes and edge failures.
High-accuracy vision systems that inspect surgical instruments for micro-cracks, early-stage corrosion and structural misalignment — extending into 3D structural analysis of orthopedic implants and their bearing surfaces.
Adaptations of the same toolkit into new domains. These are R&D engagements rather than off-the-shelf deployments.
Our pipelines adapt to thermal and infrared imagery, surfacing anomalies that are invisible in the visible spectrum — failing cells and open circuits across solar farms, or heat signatures on high-value assets.
Lightweight models optimised for edge devices, tracking erratic targets: monitoring crop health across a field, or detecting traces of pollen on individual bees for pollination tracking.
Real-time multi-class detection at production-line speed.
Pixel-level segmentation of irregular, unbounded degradation.
Promptable segmentation without per-class training data.
Global context over fine-grained surface texture.
Where this applies, indexed by sector. Open any branch to see the assets we instrument, what degrades on each, and the sensing stack used to catch it. Every asset carries a confidence tier so you can tell delivered work from research interest.
Thermography and optical inspection detect hot spots, coating over-thickness and substrate exposure.
Models analyse pre- and post-flight thermal protection systems to assess aerodynamic heating and ablation rates.
Automated 3D scanning and optical segmentation verify nozzle structural integrity after extreme exhaust exposure.
Drone imaging and U-Net segmentation map exterior wear without prolonged dry-docking.
Visual navigation models assess external wear and assist autonomous docking manoeuvres.
Semantic segmentation classifies Level of Fouling (LoF 0–5) to optimise cleaning schedules and fuel efficiency.
Underwater optical inspection detects marine growth and deformation that degrade propulsive thrust.
Binarisation and grayscale matrix analysis quantify pitting density of surface roughness.
Pixel-level annotation tracks intake blockages that threaten internal ship cooling systems.
Automated subsea imaging measures remaining anode volume to predict cathodic protection lifespan.
Edge AI and photogrammetry measure loss of cutter radius and contact surface area to time replacement.
YOLO-family models monitor moving belts in low light, detecting tears and halting systems before catastrophic failure.
Cross-correlation and spatial moment edge detectors quantify pixel change between original and worn bit profiles.
ViTs and CNNs analyse end-milling tools to predict surface finish quality and prevent out-of-tolerance machining.
Deep learning inspects extruded aluminium, tracing longitudinal die lines and scratches back to specific die degradation.
Models built from CAD designs detect cosmetic and dimensional blemishes caused by mold wear under high-tonnage pressure.
Automated optical systems inspect complex internal and external thread geometries for structural integrity and assembly viability.
Drone-captured imagery and segmentation models identify erosion, cracking and impact damage across expansive wind farms.
Infrared and RGB imagery detect failing cells, short circuits and environmental wear that reduce energy yield.
Attention-mechanism networks (CBAM) filter natural backgrounds to isolate component wear on high-voltage lines.
AI-driven visual inspection assures the integrity of critical containment vessels and steam generators.
Rig-floor cameras continuously monitor the drill string to detect dangerous vibration and optimise penetration rates.
Custom CNN architectures classify pipeline imagery to identify localised pitting and structural weakness before leaks occur.
Cameras monitor belts for micro-tears that could harbour microbial growth or shed physical contaminants into food.
Vision systems inspect meat and bakery slicers to ensure uniform cuts and prevent metal fragments in product.
High-resolution imaging detects incomplete closures, contaminated seals and equipment wear that compromise hermetic packaging.
Cab-mounted cameras apply mathematical morphology and YOLO variants (LGR-Net) to detect rail wear and clamp defects.
Camera-equipped maintenance cars survey public infrastructure continuously, geotagging defects by severity and scheduling repair work automatically.
Real-time AI classification inspects instruments to prevent retained debris or mechanical failure during surgery.
Scale-invariant feature transforms categorise wear particles and assess physical degradation of artificial joints.
Drones and fixed cameras analyse structural load-bearing elements to detect early signs of material fatigue.
Robots equipped with vision navigate subterranean pipes to detect chemical wear and physical breaches in ageing sewers.
Contact us for a technical consultation to see how our custom AI architectures can automate your monitoring processes.
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