6 Proven Computer Vision AI Use Cases for Ports
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Port terminals handle container lifting, vehicle movements and cargo operations simultaneously, often within shared working areas. The International Cargo Handling Coordination Association (ICHCA) analysed nearly 500 fatal incidents involving shore-side cargo handling workers since 2000 and found crush by cargo accounted for about a quarter of the fatalities, vehicle and pedestrian impacts for 23%, mostly involving pedestrians and mobile handling equipment, and that about three-quarters of incidents occurred during cargo operations.
Computer vision in port operations uses AI to interpret CCTV footage and identify visible safety risks, including workers entering crane exclusion zones, unsafe vehicle–pedestrian interactions and missing protective equipment. viAct Port Safety Solution apply camera-based monitoring across quay operations, container yards and terminal facilities.
This article examines six computer vision use cases, explaining what the cameras identify, how detection events are generated and what port operators can learn from the recorded footage.
What Is Computer Vision in Port Operations?
Computer vision is a branch of artificial intelligence (AI) that enables a platform to interpret images and video. In port operations, it can identify workers, vehicles, equipment and selected unsafe conditions from camera footage, then evaluate their movement or position against predefined rules.
For example, a camera overlooking an RTG operating area may identify a worker walking between container rows. If the worker crosses a configured red-zone boundary, the software can classify the movement as an intrusion event and save the relevant footage.
Unlike ordinary CCTV review, this process allows safety teams to find relevant footage without manually searching through every recording. However, the accuracy of each detection depends on the model, camera view and operating conditions.
What Are the 6 Main Computer Vision Use Cases for Port Operations?
Computer vision supports port safety by identifying workers, vehicles, equipment movements and visible hazards across quay operations, container yards and terminal facilities. Six major applications include STS Crane & Quay Monitoring, RTG Red Zone Detection, Terminal Vehicle Monitoring, Stevedore PPE Detection, Terminal Gate Monitoring and Container Fire Detection.
Ship-to-shore (STS) crane operations involve suspended containers, moving lifting equipment and workers operating close to quay edges. During loading and unloading, workers may enter crane exclusion zones or approach active lifting areas, creating struck-by and falling-object risks. Monitoring these movements becomes difficult when multiple cranes operate simultaneously across a busy quay.
Vision-based AI uses person detection, object tracking and zone monitoring to detect predefined lifting zones and quay-side boundaries. It can also support verification of the 3-3-3 lifting protocol, which involves an initial 0.3 m lift, a 3-second hold and a 3 m worker clearance distance, where the required measurements are available through calibrated video or equipment data.
What it monitors | Safety outcome |
Workers entering STS crane exclusion zones | Flags unsafe access during container lifting |
3-3-3 lifting checks: 0.3 m test lift, 3-second hold and 3 m worker clearance | Supports verification of prescribed lifting precautions |
Workers beneath suspended loads using person detection and tracking | Identifies exposure to falling-object hazards |
Restricted quay-edge access using virtual boundaries | Flags unsafe entry into monitored edge zones |
Limitation: Detection accuracy depends on camera positioning and unobstructed views. Suspended containers, crane structures and overlapping lifting activities may temporarily conceal workers or monitored boundaries.
Rubber-tyred gantry (RTG) cranes operate between container stacks, where equipment travel lanes and pedestrian routes may be close together. Workers crossing restricted areas or taking shortcuts between container blocks can enter the path of moving cranes, particularly where stacked containers obstruct the operator's view.
Computer vision monitors RTG operating areas through virtual red-zone boundaries, person detection and object tracking. When workers enter configured restricted zones or cross designated equipment lanes, the system records the event for verification. These records also help identify recurring pedestrian intrusions at specific container blocks.
What it monitors | Safety outcome |
RTG red-zone intrusions | Identifies workers entering restricted crane operating areas |
Pedestrian crossings through equipment lanes using virtual tripwires | Flags breaches of designated pedestrian routes |
Worker proximity to moving RTG cranes using object tracking | Supports safer separation between workers and equipment |
Repeated red-zone entries by location or shift | Helps identify gaps in pedestrian segregation |
Limitation: Detection depends on camera visibility and coverage. Container stacks, moving cranes and changing yard layouts may obstruct workers or monitored boundaries, requiring adjustments to camera placement and detection zones.
Container terminals have continuous movement of terminal tractors, container trucks, forklifts and pedestrians, often through shared intersections and narrow operating lanes. Reversing movements, vehicle blind spots and unexpected pedestrian crossings create collision risks, especially during peak cargo-handling periods.
Vision systems use vehicle and pedestrian detection, object tracking and proximity monitoring to identify unsafe interactions. Vehicle-mounted multi-camera systems can extend coverage around blind spots, while configurable safety zones, including a 1 m proximity threshold, can trigger alerts when workers enter a monitored vehicle boundary. Speed monitoring can also be supported through calibrated video analytics or vehicle telemetry.
What it monitors | Safety outcome |
Vehicle–pedestrian interactions using proximity detection | Flags potential collision risks in shared traffic areas |
Entry into a configurable 1 m vehicle safety zone | Alerts operators when workers cross a defined proximity threshold |
Reversing and blind-spot interactions using multi-camera monitoring | Identifies workers in monitored vehicle blind spots |
Speed-limit compliance using calibrated telematics | Flags potential speeding violations |
Limitation: Detection performance may be affected by vehicle blind spots, camera vibration, poor lighting and temporary obstructions from containers or nearby equipment. Proximity measurements also require appropriate camera calibration.
Stevedores, lashers, checkers and quay maintenance crews work around moving containers, lifting equipment and active cargo-handling areas. PPE requirements may vary according to the activity and location, while periodic inspections cannot consistently capture missing protective equipment across every shift.
Computer vision uses person detection and visual PPE classification to identify supported protective equipment, including safety helmets and high-visibility clothing. When combined with restricted zone monitoring, it can monitor whether workers entering designated operational zones have the required visible PPE and record potential violations for review.
What it monitors | Safety outcome |
Safety helmets and high-visibility clothing using AI PPE detection | Identifies potential PPE compliance violations |
PPE compliance within designated work zones | Flags workers entering monitored areas without required visible PPE |
PPE compliance during lashing and quay-side activities | Supports verification of activity-focused PPE compliance |
Repeated PPE violations across shifts and work areas | Helps target inspections and corrective actions |
Limitation: Detection accuracy depends on camera angle, lighting and worker visibility. Crowded work areas, overlapping workers and partially concealed PPE may result in missed detections or false alerts.
Terminal gates are where external container trucks, contractor vehicles and service vehicles meet the terminal’s internal traffic, and where drivers and pedestrians often share the same few access lanes. Queues, wrong-way entries and people crossing between moving vehicles create collision risks, particularly at peak gate periods.
Computer vision combines vehicle detection, direction-based tracking, pedestrian detection and virtual tripwires to identify unsafe movements around gates and controlled routes. Automatic Number Plate Recognition (ANPR) when added to link an event to the vehicle involved and to its approved access record, safety teams can follow up with the haulier or driver and see whether a vehicle is using a route it has not been approved for.
What it monitors | Safety outcome |
Wrong-way vehicle movements using direction-based detection | Flags vehicles travelling against the designated traffic flow |
Pedestrians entering vehicle lanes or restricted crossings using virtual tripwires | Flags pedestrian exposure to moving traffic |
Vehicle registration using Automatic Number Plate Recognition (ANPR) | Links repeated unsafe movements to the vehicle involved for follow-up |
Vehicle entry against approved access records | Helps flag vehicles entering controlled cargo-handling routes without approval |
Limitation: Detection performance depends on clear camera views of vehicles, lanes and registration plates. Poor lighting, rain, vehicle queues, obstructed plates and unsuitable camera angles may reduce accuracy.
Container yards, reefer blocks and container freight station warehouses contain cargo, electrical equipment and other potential fire hazards. Large storage areas, densely stacked containers and limited sightlines can make visible smoke or flames difficult to identify quickly through manual observation alone.
The AI-based vision system uses video-based smoke and flame detection to identify potential fire indicators within monitored areas. Area-specific detection rules and location-based alerts help direct attention to affected container blocks, reefer operations and hazardous cargo storage areas for verification and emergency response.
What it monitors | Safety outcome |
Visible smoke using AI video smoke detection and IoT sensors | Flags potential fire indicators for investigation |
Visible flames using computer vision classification | Supports prompt notification of visible fire hazards |
Smoke and flame events near reefer container blocks | Identifies visible fire risks around refrigerated container operations |
Smoke or flames near designated hazardous cargo storage areas | Supports location-specific fire hazard monitoring |
Limitation: Detection depends on visibility and environmental conditions. Dense container stacks, heavy rain, fog, low lighting and interference from steam or vehicle exhaust may affect smoke and flame recognition.
What Proven Results Have Port Computer Vision Deployments Reported?
Across its port operations in FY2024 to FY2026,as of September 2026, viAct reported a 95% reduction in crane-related incidents, covering overload, swing and rigging-failure events, and an 80% reduction in lifting errors. Over the same period, more than 12,000 pre-lift alerts identified 97% of critical rigging issues.
At Hong Kong’s Kwai Chung Container Port, viAct published case study describes a deployment combining lift-zone monitoring, fatigue alerts, PPE detection and a digital permit-to-work system, used to support compliance with Marine Department safety requirements. The port reported a 10x improvement in its lift-zone safety score, a 60% drop in fatigue-linked errors and a 50% improvement in yard productivity through fewer incident-related stoppages. The deployment also used AI to check that lifts followed the 3-3-3 protocol: a 0.3 m lift, a 3-second hold and 3 m of worker clearance. In this 24/7 operation, the Hong Kong company also reported recovering more than 120 hours of incident-related stoppages each month.
These are viAct-reported results. They measure different things: incident and lifting-error reductions are safety outcomes, pre-lift alerts and identified rigging issues reflect detection activity, and yard productivity and recovered stoppage hours reflect operational continuity. They should not be compared directly with one another, and each terminal should measure against its own baseline.
When evaluating results like these, ask what baseline period they were measured against, how a crane-related incident or lifting error is defined and who recorded it, whether results are normalised per 1,000 lifts, and whether alerts were verified by a person before being counted.
Conclusion: Key Takeaways
Computer vision supports six major port applications: STS Crane & Quay Monitoring, RTG Red Zone Detection, Terminal Vehicle Monitoring, Stevedore PPE Detection, Terminal Gate Monitoring and Container Fire Detection.
Detection methods vary by application. Person detection, object tracking, virtual boundaries, proximity monitoring and ANPR address different operational risks.
Camera placement influences reliability. Container stacks, moving equipment, poor lighting and adverse weather can affect visibility and detection performance.
Event records can support preventive action. Repeated red-zone intrusions, traffic conflicts and PPE violations help identify locations and activities that require closer investigation.
Performance should be measured against operational activity. Verified incident rates, lifting errors, near misses and downtime provide more meaningful evidence than alert counts alone.
Computer vision should be evaluated as part of a wider port safety management process. Its effectiveness depends not only on detecting hazards but also on how consistently those detections are verified, investigated and translated into corrective action.
Quick FAQs
1. What can computer vision detect in port operations?
Computer vision can detect workers entering crane exclusion zones, RTG red-zone intrusions, vehicle–pedestrian interactions, missing visible PPE, restricted-area crossings and visible smoke or flames. These applications use object detection, movement tracking, virtual boundaries and visual classification to identify predefined safety events in port camera footage.
2. Can computer vision work with existing CCTV cameras at container terminals?
Yes. Platforms like viAct often use existing IP CCTV cameras that support RTSP video streaming. For reliable detection, cameras should provide adequate resolution, suitable viewing angles and sufficient lighting. A 1080p resolution at 25 fps is a useful baseline for evaluating camera compatibility, although individual applications may require different specifications.
3. How accurate is it in busy container terminals?
Computer vision accuracy depends on the detection task, camera placement, lighting, weather, object visibility and site configuration. Container stacks, moving cranes and overlapping workers can affect detection performance. Ports should evaluate each application using verified detections, missed events and false-positive rates rather than relying on a single overall accuracy percentage.
4. How long does it take to deploy and calibrate computer vision at a port?
Deployment time depends on the number of cameras, monitored areas, detection applications and required integrations. A limited pilot may begin with one crane bay, RTG operating area or vehicle crossing. Camera coverage, detection zones and alert thresholds should then be tested under actual operating conditions before expanding to additional terminal areas.
5. How should port operators evaluate the cost and ROI of computer vision?
Port operators should assess the number of cameras, required AI applications, processing infrastructure, deployment model, integration work and ongoing support. ROI can be evaluated through changes in verified safety incidents, lifting errors, investigation time and incident-related downtime. A pilot with predefined performance indicators provides a more reliable basis for estimating value than projected savings alone.
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