A special survey dry dock for a mid-size commercial vessel runs USD 2 to 5 million and takes 20 to 30 days off-hire, and a large share of that visit is spent on inspection work that a diver or an ROV could already document while the ship stays afloat. Classification societies have permitted in-water surveys as a substitute for a scheduled dry-docking for years, but the underwater footage behind most of those surveys is still reviewed by eye, one clip at a time, by a surveyor working from a video feed. AI vision applied to that same underwater footage turns a manually reviewed clip into a structured, defect-tagged hull and propeller record, built to support the survey rather than replace the surveyor's sign-off. To see how this fits your vessel's survey cycle, book a demo.
SUBSEA INSPECTION · HULL & PROPELLER · DRY DOCK ALTERNATIVE
Everything a Dry Dock Would Tell You, Without Taking the Ship Out of the Water
Hull condition, propeller damage, and coating integrity can now be documented underwater, in class-recognized detail, while the vessel stays in service. AI vision structures that footage into a defect-by-defect record a surveyor can act on.
THE ECONOMICS OF ONE AVOIDED DOCK VISIT
What a Dry Dock Actually Costs, Beyond the Yard Invoice
Dry-docking is a fixed regulatory requirement, not something a fleet can skip entirely, but classification rules already allow an in-water survey to substitute for one docking cycle when the underwater documentation is thorough enough to stand in for it.
$2–5M
Typical special survey dry-docking cost for a container-class vessel, yard work included
20–30 days
Off-hire duration for a scheduled special survey dry-docking window
~7%/yr
Approximate charter revenue impact attributed to off-hire time across a dry-docking cycle
$200K–1M
Additional cost range when unexpected steel renewal is discovered only once the vessel is already docked
TWO SURFACES, TWO FAILURE MODES
What the Camera Is Actually Looking For
Hull and propeller inspection are often talked about as one task, but they fail in different ways and are inspected against different standards. AI vision is trained to evaluate each on its own terms rather than applying one generic underwater defect model to both.
H
Hull Plating and Coating
The hull is inspected for plating condition, coating breakdown, corrosion, mechanical damage, and biofouling coverage across a large, mostly flat surface area. Biofouling is the dominant recurring finding: even a light slime layer raises hydrodynamic drag enough to increase fuel consumption by a meaningful margin, and heavier growth compounds that penalty further the longer it goes unaddressed.
Coating breakdown
Corrosion and pitting
Biofouling coverage class
Mechanical damage
P
Propeller and Running Gear
Propellers operate at high rotational speed, which means even minor surface fouling or a small edge deformity does more relative damage per square inch than the same condition would on the hull. Roughness on the blade surface can trigger cavitation, which erodes metal and reduces thrust, making early-stage detection on the propeller a higher-leverage catch than the equivalent finding on the hull.
Blade edge damage
Cavitation erosion
Fouling on running gear
Shaft seal condition
See what AI-structured hull data looks like for your fleet
iFactory configures hull and propeller inspection around your vessel classes, your survey cycle, and your existing ROV or diver footage — not a generic underwater defect model.
DIVER-REVIEWED FOOTAGE VS. AI-STRUCTURED FOOTAGE
What Changes When the Footage Gets Analyzed, Not Just Recorded
Underwater inspection footage already gets captured on most in-water surveys today. The gap is what happens to it afterward — whether it sits as an unindexed video file or becomes a structured, searchable defect record tied to a specific hull location.
| Inspection Factor |
Manually Reviewed Video |
AI-Structured Inspection |
| Coverage consistency |
Depends on diver attention and dive time budget |
Every frame analyzed against the same defect standard |
| Defect location record |
Verbal or written notes tied loosely to footage timestamps |
Defects tagged to hull zone and frame automatically |
| Comparability across surveys |
Depends on the reviewing surveyor's notes matching prior format |
Consistent tagging enables direct survey-to-survey comparison |
| Turnaround to a usable report |
Manual review and report writing after the dive |
Structured defect summary generated from the footage itself |
| Biofouling severity grading |
Qualitative description from the reviewer |
Consistent severity classification supporting cleaning-cycle decisions |
FROM UNDERWATER FOOTAGE TO SURVEY-READY RECORD
How a Dive Becomes a Structured Inspection Report
The workflow builds on the underwater survey process already in place, adding a structured analysis layer to footage that is being captured anyway.
1
ROV or Diver Captures Underwater Footage
A tethered ROV or a diver-mounted camera records the hull and propeller following the vessel's approved underwater survey plan, the same footage already required for an in-water survey submission.
2
Footage Mapped to Hull Zones
Frames are aligned to a hull reference grid so every defect finding can be tied to a specific location rather than a loose timestamp in a long video file.
3
Defect and Fouling Detection
The vision model flags coating breakdown, corrosion, mechanical damage, and biofouling on the hull, and blade damage or cavitation erosion on the propeller and running gear.
4
Findings Compiled Into a Structured Report
A defect-by-defect summary with location, severity, and supporting frame imagery is compiled in a format built to support the surveyor's review, not to bypass it.
5
Survey-Cycle Comparison Over Time
Because each survey is tagged the same way, this inspection's findings can be compared directly against the vessel's prior survey, showing fouling accumulation rate and coating degradation trends across cycles.
WHERE THIS FITS IN THE CLASS SURVEY CYCLE
AI-Structured Inspection Supports the Surveyor, It Doesn't Replace Them
Classification societies already permit an in-water survey in place of one scheduled dry-docking within a five-year class period, provided the vessel holds the appropriate survey notation and the underwater documentation gives the surveyor the same information a dry-docking would. That approval process, and the surveyor's judgment during the review, does not change with AI-structured footage — what changes is the quality and consistency of the material the surveyor is working from.
A structured, zone-tagged, severity-graded defect record is easier for a surveyor to review quickly and harder to under-document by accident than an unindexed dive video reviewed once and summarized from memory. This matters most on the intermediate survey cycle, where the case for an in-water survey depends on demonstrating the underwater documentation is genuinely equivalent to what a dry-docking would show — the more complete and structured that documentation is, the stronger that case is going into the class society's review.
BEFORE YOU START
What Fleet and Technical Superintendents Should Have Ready
Operators that move fastest from evaluation to a working pilot generally arrive with a clear picture of their fleet's survey status and existing underwater inspection practice, rather than starting the conversation without that context.
1
Vessel Class and Survey Status
Which vessels currently hold or are eligible for an in-water survey notation, and where each one sits in its five-year class period.
2
Current Underwater Inspection Method
Whether inspections currently run on diver-mounted cameras, a contracted ROV service, or an owned ROV fleet, since footage source shapes how the analysis layer is integrated.
3
Historical Fouling and Coating Data
Any existing record of biofouling severity, cleaning cycle timing, or coating performance by vessel, which becomes the baseline a pilot is measured against.
4
Class Society and Flag State Contacts
Who manages the in-water survey application and approval process for your fleet, since structured documentation is built to support that submission.
Scope a pilot around your fleet's survey cycle
A short working session maps your vessel classes, current inspection method, and survey timing against what a pilot deployment would look like for your fleet.
FREQUENTLY ASKED QUESTIONS
What Fleet Operators Ask Before Adopting AI-Structured Hull Inspection
Does this replace the class surveyor or let us skip a required survey entirely?
No. Dry-docking and in-water surveys remain governed entirely by your classification society and flag state, and a surveyor's approval is still required for any survey outcome, including whether a vessel qualifies to substitute an in-water survey for a scheduled docking. What AI-structured analysis changes is the quality and consistency of the underwater documentation the surveyor reviews, turning a long dive video into a zone-tagged, severity-graded defect record that is faster and more consistent to review. The class society's approval process and requirements are unaffected.
Contact our support team to see how structured reporting fits alongside your existing survey submission process.
Do we need to buy a new ROV or can this work with our existing inspection contractor's footage?
The analysis layer works with footage from your existing ROV, whether that is an owned unit, a contracted underwater inspection service, or diver-mounted cameras, provided the footage resolution and lighting meet the minimum standard for reliable defect detection. Most fleets do not need to change their underwater data collection method to adopt structured analysis, since the change happens after footage capture rather than requiring new hardware in the water. Footage quality is assessed against detection requirements as part of pilot scoping before any hardware change is recommended.
Book a demo to check compatibility with your current inspection setup.
How does the system tell the difference between light biofouling and something that needs urgent attention?
Biofouling is graded on a severity scale reflecting coverage density and growth stage, from an early slime layer through hard macro-fouling like barnacles, since these stages carry meaningfully different fuel-consumption and cleaning-urgency implications. Even a light slime layer is known to raise drag enough to increase fuel costs by a noticeable margin, while heavier fouling compounds that penalty further, so the severity grade is built to support a cleaning-cycle decision rather than a simple clean-or-dirty flag. The same grading logic applies separately to the propeller, where fouling and roughness carry a higher performance penalty per square inch than on the hull.
Contact our support team to review how severity grading would apply to your fleet's cleaning schedule.
Can this detect early-stage cavitation damage on the propeller before it affects performance?
Cavitation erosion typically begins as small pitting or roughness on the blade surface, well before it becomes visible as significant metal loss or a measurable drop in thrust, and this early stage is exactly where a vision model trained on propeller-specific defect signatures can add the most value over a general visual pass. Because propeller surfaces are inspected against a different defect profile than the hull, detection is tuned specifically to blade edge condition, surface roughness, and erosion patterns rather than reusing a hull-focused model. Catching this early keeps propeller polishing a routine maintenance item rather than a reactive fix after a performance drop.
Book a demo to see propeller-specific detection on sample footage.
How long does it take to get a fleet's first pilot inspection analyzed and reported back?
Timelines depend on fleet size, footage volume, and how many vessel classes are in scope, but a scoped pilot on a small number of vessels is generally the fastest way to validate detection accuracy against your fleet's own historical inspection data before wider rollout is considered. Because the system works from footage you are already capturing during scheduled underwater surveys, most of the pilot timeline goes toward calibrating detection to your specific hull coatings and vessel classes rather than new data collection.
Contact our support team to scope a pilot timeline for your fleet.
DOCUMENT THE HULL, KEEP THE SHIP IN SERVICE
Bring Structured Underwater Inspection to Your Survey Cycle
Hull and propeller condition drive fuel cost, survey outcomes, and dry-docking timing alike. iFactory configures AI-structured inspection around your fleet's vessel classes, survey cycle, and existing underwater footage.
