ConductVision Enterprise
ConductVision logs when aircraft, service trucks and bag carts arrive at and leave each stand, and times cars at the departures curb, for airport and ground handling teams.
10 detectionsConductVision detections on this photo.
Airport environments demand exceptional operational precision where ground hazards, passenger flow bottlenecks, and security threats must be managed simultaneously across airside and landside operations. Foreign Object Debris on runways, aircraft ground damage events, baggage handling errors, and terminal overcrowding all carry severe safety and financial consequences. ConductVision's technology can provide comprehensive visual analytics across airport operations, from runway and apron monitoring to terminal crowd management and baggage handling verification. Real-time detection of FOD, ground equipment conflicts, and passenger flow anomalies can enhance safety and operational efficiency across the airport ecosystem.
Outcomes
Ground handlers log turnaround steps by hand, if they log them at all.
When a flight leaves late, no one can show which service ran long.
Stand planners assign gates from the schedule, and actual on-stand times drift from it.
A late departure holds the stand and sends the next arrival to a remote stand or a wait.
Cars stop at the departures curb past the posted limit during peaks.
Long stops back up the curb road and slow every drop-off behind them.
Runway FOD causes millions in aircraft engine and tire damage annually, yet detection depends on scheduled vehicle inspections that leave gaps between passes. Ground operations on the apron involve complex vehicle and equipment choreography where collisions and aircraft ground strikes are persistent risks. Terminal operations face unpredictable passenger surges that overwhelm security checkpoints and gate areas.
ConductVision can process feeds from runway-edge cameras to detect FOD between inspection passes, providing continuous coverage that supplements vehicle-based sweeps. Apron cameras can monitor ground vehicle traffic, aircraft pushback clearances, and equipment positioning. Terminal cameras can generate real-time passenger density maps and queue predictions for checkpoint and gate area management.
What the model sees
ConductVision detections on this photo. Photo: Carlos A. Gaviria-Mendoza, CC BY-SA 4.0, cropped.
ConductVision detections on this photo. Photo: Carlos A. Gaviria-Mendoza, CC BY-SA 4.0, cropped.
Endpoints
Each endpoint is defined before the pilot starts, with what is counted, its unit and how the results are broken down.
| Endpoint | How it is counted | Unit | Broken down by |
|---|---|---|---|
| PrimaryTurnaround time | Minutes from an aircraft's first frame on stand to its last, per turnaround. | Unit:minutes (median) | By:Stand, hour |
| SecondaryService time at stand | Minutes each service truck or bag cart spends at the stand during a turnaround. | Unit:minutes (median) | By:Service, stand |
| Stand occupancy | Share of each hour a stand has an aircraft on it. | Unit:% | By:Stand, hour |
| Curb dwell | Minutes each car stays stopped at the departures curb. | Unit:minutes (median) | By:Door, hour |
Each pilot measures ConductVision on your own footage against a sample checked by hand, before anyone relies on its numbers.
Milestone time error
Difference between an arrival or departure time ConductVision logs and a reviewer's timestamp on sampled turnarounds.
seconds (median absolute difference) · by stand, milestone
Cart recall
Share of bag carts a reviewer sees in sampled frames that ConductVision also detected.
% · by stand, light conditions
Missed turnarounds
Turnarounds a reviewer finds in sampled footage that ConductVision did not log, per stand per week.
turnarounds per stand-week · by stand
Capabilities
Counts and tracks people in each camera view, by zone and hour.
Classifies, counts and tracks cars, trucks, buses, motorcycles and bicycles.
Counts dense crowds and maps where people gather, section by section.
Tracks posture, waiting and movement from pose keypoints in public spaces.
Use cases
Cameras positioned along runway edges and taxiway intersections can detect foreign objects including hardware, luggage components, wildlife, and pavement fragments between scheduled FOD inspections. Detections can be classified by size and location, with alerts routed to airfield operations for rapid removal.
Apron cameras can monitor ground service equipment movement around parked aircraft, detecting proximity violations and potential collision trajectories with fuselage, wings, and engines. Alerts can reach ramp controllers before contact occurs, and incident near-miss data can inform ground handling training programs.
Cameras at sort points, make-up carousels, and container loading positions can verify bag routing, detect jams and mishandled items, and confirm container integrity before transport to aircraft. Misrouted bags can be flagged early in the handling chain, reducing misconnects and improving on-time baggage delivery.
Cameras at security checkpoints, immigration counters, gate areas, and retail zones can provide real-time passenger density and queue length data. Predictions can enable dynamic lane opening at security checkpoints and staffing adjustments at immigration counters before queues reach service level thresholds.
Cameras can verify that ground crews wear required high-visibility vests and hearing protection, that vehicle speed limits are observed on the apron, and that jet blast exclusion zones are respected during engine start. Compliance data can be reported by airline, handler, and shift for safety performance benchmarking.
Related
Tracks forklifts, pallets, trucks and people from dock door to rack.
Counts people and vehicles at public entrances and inspection lanes.
Counts people, vehicles and bikes at intersections, stops and public spaces.
Pilot
Tell us about your sites and cameras, and a ConductVision engineer replies with a scoping plan built around the endpoints above.
