Qatar Open Innovation

Challenge Statement

The 3-Minute Revolution: Streamlining an Autonomous, Real-Time Container Damage & Cost Estimation System

How might we automate container gate inspections to detect damage, document evidence, and generate repair estimates in under 3 minutes per container?

GWC is seeking solution providers to develop an automated, scalable inspection approach that can operate in Qatar's outdoor depot conditions. While current manual processes at the gate are relatively fast, peak times lead to operational congestion. There is currently no system enabling continuous 24/7 gate intelligence that provides consistent repair estimates across different shipping line criteria. GWC prefers solutions that address the entire workflow—from capture to reporting—integrating with existing depot management and workshop systems.

Your solution should combine advanced imaging technologies (such as fixed gantry imaging, LiDAR, or computer vision) with automated analytics. It must deliver actionable insights into damage classification—including structural dents, corrosion, and seal damage—through a clear 3D representation of the container with timestamped photographic evidence. The system should provide real-time repair cost estimates that materially reduce missed damage and disputes compared to the manual baseline.

The goal is to enhance operational throughput, achieve a positive ROI within 12 to 18 months, and improve logistical efficiency through a transparent, data-driven, and scalable container management solution.

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How might we automate container gate inspections to detect damage, document evidence, and generate repair estimates in under 3 minutes per container?

Container depots are critical nodes in global maritime logistics. At the gate-in and gate-out points, depots must identify and inspect each empty container, reefer, and ISO tank, document any damage, and prepare repair estimates that align with the relevant shipping line’s criteria and tariffs. These inspection outcomes directly affect liability, repair authorisations, billing, and overall container turnaround time.

At GWC’s container yard operations in Qatar, this process is currently performed manually and dependent on a team of trained surveyors. Containers are received during daylight and nighttime, and surveyors inspect the exterior, the interior (by opening the doors and inspecting), the roof and the understructure, which often requires lifting the container to check for damage underneath. Dents that are considered as damage vary by depth and by client specification.

The current manual process is relatively fast (typically 7–8 minutes per container), but volumes can reach 250–300 containers per day. Peak times at the gate, therefore, lead to truck queues and operational congestion. GWC’s ambition is to reduce inspection time to 2–3 minutes per container while increasing accuracy and completeness.

Beyond speed, inconsistency is a major pain point. Different shipping lines apply different damage limits, codes, and rates, and many require updates in their own maintenance & repair systems/portals. Errors or missed damage can lead to disputes Currently missing is a representation of a container with damage pin pointed along with evidence.

GWC is therefore seeking solution providers to develop an automated, scalable inspection approach that can operate in Qatar’s outdoor depot conditions. Produce defensible evidence, automatically identify damage, showcase a 3D container model, identify shipping container types and liners. Also integrating with depot workflows, and support consistent repair cost estimation.

We are looking for solutions providers that use any suitable technology approach (e.g. fixed gantry imaging, mobile imaging, LIDAR, computer vision, 3D sensing, etc.) and address the entire workflow from capture to reporting.

Technical requirements:

Damage detection: detect and classify major damage types, including structural damage (dents/bends/buckles), surface damage (scratches/abrasions), corrosion (rust/pitting), holes/perforations, door/seal damage, roof damage, and floor damage (when visible). During the pilot phase, regular containers are in scope; for a full commercial roll-out, reefers and ISO-tanks are also in scope.
Detection accuracy: damages as small as 2cm should be detected by the system, and, over time, detection accuracy should exceed 95% for significant damage and materially reduce missed damage (versus the current manual baseline).
Damage location: the damage location should be logged precisely.
Operating conditions: the solution should operate reliably in outdoor GCC conditions (heat, dust, humidity, rain, and sandstorms), variable lighting (day, night, and harsh shadows), and dirty containers.
Container detection and metadata capture: container identification (BIC codes), container type and size and any other markings should be read and recorded (>99% accuracy).
Repair cost estimation: the solution should automatically generate repair cost estimates in line with IICL guidelines and support shipping line specific criteria, damage codes and tariffs while providing a clear cost breakdown. Over time, consistency in repair cost estimates should achieve 10% variance for similar damage scenarios.
Documentation outcome: a clear 3D representation of the container with damages and photo evidence is expected.
Workflow: integrate or replace GWC’s depot management system, shipping line and repair workshop systems, and the ability to email reports.
Auditability: damage reports should contain timestamped photographic evidence and offer a secure audit trail.

Performance Criteria:

Detection accuracy: the main driver, also from a performance perspective, especially in the pilot phase. Also, refer to the technical requirements above.
Inspection time: over time, achieve a 60 to 80% reduction in inspection time (aiming for 2-3 minutes per container).
Gate throughput: the goal is to achieve 30+ containers per hour per lane to reduce truck waiting times during peak hours.
Cost estimation disputes: the target is a reduction of over 70% in damage-related disputes.
Inspection costs: per-container inspection costs should be competitive with (or below) the current manual inspection cost when fully deployed.
ROI: we aim for a positive ROI from the fully deployed solution within 12 to 18 months, so we can recover hardware costs through operational savings (labour efficiency, reduced disputes, etc.).

Cost targets for the commercial phase will be determined on a case-by-case basis and depend on the ability to achieve the stated performance criteria (ROI, inspection costs, reduction in disputes, etc.).

Phase 1: POC development: Q3 – Q4 2026
Phase 2: Commercial roll-out: to be determined on a case-by-case basis

If the solution is successful, GWC Logistics is willing to support a further roll-out across lanes and depots.

Cash contributions:
QRDI to provide a cash contribution of up to US$100,000 for pilot development.

In-kind contributions:

Mentorship and support for solution development, access to relevant datasets (NDA required), and access to a pilot lane with parallel testing opportunities.

We are looking for SMEs and startups with solutions that can be implemented in a relatively short timeframe (TRL 5 or higher).

Background IP (BIP): Each party shall retain all rights, titles, and interests in their respective BIP introduced to the project.
Foreground IP (FIP) Ownership: GWC Ownership: GWC shall retain all IP rights to AI models trained or fine-tuned specifically using GWC’s proprietary data. Solution Provider Ownership: The Solution Provider shall retain ownership of all other FIP created during the project, including training and fine-tuning methodologies.
Commercialization & Territorial Rights: GCC Region: GWC shall have the exclusive right to sell, license, and commercialize the FIP within the Gulf Cooperation Council (GCC) member states. Rest of World (RoW): The Solution Provider shall retain the exclusive right to sell and commercialize the FIP in all territories outside of the GCC.

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3-Minute Express: Automated Gate Intelligence for Damage Container Challenge

How might we automate container gate inspections to detect damage, document evidence, and generate repair estimates in under 3 minutes per container?

Challenge Statement

How might we automate container gate inspections to detect damage, document evidence, and generate repair estimates in under 3 minutes per container?

How might we automate container gate inspections to detect damage, document evidence, and generate repair estimates in under 3 minutes per container?