Subsea coating-breakdown defect annotation.
Challenge
Detecting coating breakdown (corrosion and paint peel-off) on subsea and marine structures from inspection imagery, while distinguishing true defects from staining, artifacts and masked regions, at high volume.
Approach
On the V7 Darwin platform, io-ai annotated all coating-breakdown instances inside (and crossing) defined patch boundaries with tight polygons (corrosion, paint lifting, stripped areas, streaks), while correctly excluding staining, camera and lens artifacts, and masked or faded regions per SOP-CA-001.
A second workstream handled multi-class item annotation (tight bounding boxes, occlusion rules, generic-class fallbacks), exported in YOLO v5 format.
Quality
Every task ran through io-ai's multi-level QA pipeline, with first-batch audits before scale-up and full rejection-reason metadata on every item, holding over 98% accuracy on rolling audits, with weekly status reporting.
Result
Reliable defect-segmentation and item-classification datasets for industrial inspection models, a domain that requires real annotator training, not crowd guesswork.
This was a scaled engagement run with artifact-vs-defect calibration sessions and zoom-to-confirm patch boundaries.
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