Yards, docks and fleets under control

Automatic counting and classification of vehicles by type on roads, parking lots, yards and gates. Generates traffic data by hour, direction and category — a basis for traffic management, logistics yards, parking lots and commercial feasibility studies.

How it works

1. 1Vehicles are detected and classified (car, motorcycle, bus, truck, bicycle).
2. 2Each vehicle is tracked; crossing the virtual line increments the count.
3. 3The heat map shows the lanes and areas with the most traffic.

Benefits

• Replaces manual counts and inductive loops.
• Continuous data by vehicle category and time of day.
• A basis for dynamic parking pricing.

The AI locates the stack (or exhaust) and the smoke coming out of it, cuts out the exact outline of the plume and analyzes its color palette. Smoke color indicates combustion quality and what is being burned: white is usually just water vapor; dark gray and black indicate incomplete combustion (soot); bluish, burning oil; yellowish or brown, a sign of sulfur or NO₂. Darkness is measured on the Ringelmann scale, the same one used by environmental inspectors.

How it works

1. 1Text-prompted segmentation (CLIPSeg): in every frame the AI marks where there is smoke or vapor — without specific training.
2. 2Text-prompted detection (OWLv2) locates the stacks or exhausts and discards similar structures (distillation towers, plant buildings); the minimum confidence is adjustable.
3. 3The plume outline (not a box) is used in the analysis, so sky, buildings and the stack itself do not contaminate the colors.
4. 4Plume colors are grouped into the 4 dominant colors (k-means in CIELab space) and each is classified into a shade.
5. 5Darkness = how close the smoke is to black (0% = white, 100% = black). Ringelmann scale = darkness ÷ 20 (levels 0 to 5).
6. 6Each stack is analyzed separately (the smoke right above it; flare flames are excluded from color analysis). The indicators show the worst stack.
7. 7Persistent dark smoke (from the configured Ringelmann level) or abnormal coloring triggers an alert identifying the stack, with an image of the moment.

Benefits

• Smoke color reading → probable cause: vapor, particulates, incomplete combustion, oil, sulfur or NO₂.
• Automatic Ringelmann scale, the same used in inspections (e.g. black smoke limit for diesel).
• Continuous evidence for environmental permits and conditions (applicable emission standards).
• Immediate identification of incomplete combustion → fuel savings.

Facial monitoring of drivers and operators to identify signs of fatigue and microsleep — eyes closed for a prolonged time, rising PERCLOS, frequent yawning — sounding alarms and notifying the control room before an accident happens.

How it works

1. 1The face mesh (468 points) is computed on every frame.
2. 2The EAR (Eye Aspect Ratio) measures eye openness with a per-person adaptive threshold.
3. 3PERCLOS indicates the percentage of time with eyes closed (NHTSA standard for drowsiness).
4. 4MAR detects yawns; the combination of signals determines alert, fatigued or drowsy.

Benefits

• Prevents accidents caused by drowsiness — up to 20% of serious road accidents.
• Immediate alerts at the workstation and in the monitoring center.
• Fatigue metrics by shift for working-hours management.

Virtual fences drawn on the camera image outline hazardous areas (machine working radius, presses, overhead cranes, docks, energized areas) or restricted areas (storerooms, perimeters). When a person enters, an immediate alert is triggered — and it can sound sirens or activate the machine interlock.

How it works

1. 1The user draws the zone on the camera image (free-form polygon).
2. 2The detector identifies people and vehicles; the position of the feet determines presence in the zone.
3. 3Each entry generates an event with photo, duration and time.
4. 4Webhook/PLC integrations make it possible to stop machines or trigger signaling.

Benefits

• Prevention of serious and fatal accidents involving machines and vehicles.
• Set up in minutes, with no construction work or physical sensors.
• Evidence for investigating incidents and near misses.