Offshore Asset Inspection with Drones and Edge Object Detection
On offshore platforms, every rope-access hour is expensive and risky. Drones plus edge-deployed detection are rewriting the inspection playbook.
Erik Sandberg
Offshore Inspection Lead
Few environments are as hostile to inspection as an offshore platform. Structures are enormous, access is dangerous, and every hour of rope-access or scaffolding work carries real risk and cost. Combining drones with edge-deployed object detection is quietly transforming how operators keep offshore assets safe and compliant.
The offshore inspection problem
Inspecting a platform or FPSO traditionally means sending people to height, over water, and into confined spaces. It is slow, weather-dependent, and exposes personnel to some of the highest-risk work in the industry. Drones remove much of that exposure by reaching flare booms, underdecks, and splash zones without putting a person there.
Why detection runs at the edge
Offshore connectivity is limited and often unreliable, so streaming every frame back to shore for analysis is rarely practical. Instead, detection models run on the edge — on the drone or a nearby gateway — flagging defects locally and transmitting only the results and key frames. This is where lightweight, exportable models earn their keep.
- Corrosion and coating breakdown on structural members and handrails.
- Structural anomalies such as cracks, deformation, or missing fasteners.
- Marine growth on splash-zone and subsea-adjacent structures.
- Leaks and staining around flanges, valves, and process equipment.
Export once, deploy anywhere
A model trained in the cloud is only useful offshore if it can run on constrained hardware. That is why export formats — ONNX, TensorRT, CoreML, TFLite — matter so much. A practical workflow trains and validates centrally, then exports an optimised model for the edge device on the drone.
Resource
Yollohub supports one-click export to ONNX, TFLite, CoreML, and other edge runtimes, making it straightforward to move a trained detector from the training studio onto an inspection drone.
From flight to findings
A mature offshore programme stitches the pieces together: a planned drone flight, on-device detection, geotagged findings, and an integrity dashboard that ranks defects by severity. Inspectors then verify only the flagged items, dramatically compressing the review cycle and reducing time on the structure.
Where it is heading
The trajectory is clear: fewer people in high-risk positions, more continuous data, and detection models that improve every deployment through active learning. Operators that invest early in a repeatable train-export-deploy loop — with platforms like Yollohub handling the model lifecycle — build an inspection capability that compounds over time.
To see how the same detection stack supports integrity onshore, read our guide to automated pipeline corrosion detection.