Corrosion Detection with Vision AI
See how operators automate rust, pitting, and coating breakdown inspection across fixed assets.
Add an analysis layer to every tank and vessel survey with vision AI for storage tank and pressure vessel inspection. Built for the programs where a single external of one large tank produces thousands of frames, the certified inspector who has to review them is the actual bottleneck, and the coating failure on the north shell gets found on the pass where somebody happened to be paying close attention. Whether you're inspecting crude and product tanks, spheres and bullets, process vessels, or the containment around them, Roboflow processes the imagery your inspection program already collects and hands back defects positioned on the asset.
Shell, Coating, and Weld Condition:
Roof, Appurtenances, and Insulation:
Positioning, Records, and the Inspector's Time:
Bring intelligence to every survey today. Stop a coating failure from becoming an unplanned outage, a roof drain from becoming an SPCC finding, or a thickness reading from arriving a cycle too late.
What is storage tank and pressure vessel inspection with Vision AI?
Tank and vessel inspection with vision AI applies computer vision models to the imagery an external inspection already produces: drone survey frames, rope-access photos, handheld shots from a walkdown. Models trained on your assets detect and localize the visible conditions that drive inspection findings, including coating breakdown, external corrosion and pitting, weld seam condition, roof and seal problems, jacketing damage, and appurtenance issues, then position each finding on the asset and rank it by severity. The output is a triaged, image-backed, positioned defect list that a certified inspector works from, and a record that carries forward so progression between survey cycles is visible rather than inferred.
Can Vision AI measure wall thickness or find corrosion under insulation?
No, and both limits matter enough to state plainly. A camera measures surface condition, not metal loss. Remaining wall thickness comes from ultrasonic testing, and every remaining-life and next-inspection-date calculation under API 653 and API 510 runs on that thickness data, not on imagery. Corrosion under insulation is worse still, because by definition the damage is hidden beneath jacketing; finding it requires removing insulation, or profile radiography, pulsed eddy current, or guided wave, depending on the asset. What vision does is find the external evidence that predicts where those methods should be pointed: damaged and missing jacketing, failed banding, sealant breakdown at penetrations, and the rust staining that runs from a jacket seam. On a unit with thousands of insulated feet and a limited inspection budget, turning that evidence into a ranked CUI candidate list is the practical win, and it makes the expensive methods land where they pay.
Does this satisfy API 653 and API 510 inspection requirements?
Not on its own. API 653 and API 510 require inspections to be performed and reports certified by an inspector holding the relevant API certification, working to the intervals and procedures those codes define, and no software output substitutes for that. Roboflow sits inside that program as a tool the inspector uses: it processes survey imagery consistently, surfaces and positions conditions across the whole asset rather than the fraction anyone can review closely, and produces the documented, image-backed record that supports the inspection report, risk-based inspection scoring under API 580 and 581, and SPCC visual inspection documentation. Your integrity team owns the intervals, the acceptance criteria, the thickness data, and the certification. Vision makes the inspector faster and the coverage more complete, not the certification unnecessary.
Can it integrate with our inspection data management system?
Yes. Findings, severity ranking, imagery, and position on the asset push into inspection data management and asset integrity systems through REST and direct database writes, so a detection lands as a record against the right tank, shell course, and circuit rather than as a folder of photos. Results can carry the drone flight's positional metadata so a finding maps back to a location on the shell, and can be tied to your equipment IDs and circuit numbers for risk-based inspection scoring. Processing can run in your VPC or fully on-prem, which is the normal requirement when the imagery covers critical infrastructure and cannot leave your environment.
