Maddox AI significantly reduces inspection effort for weld and crack inspections on shipboard container cranes by automatically pre-sorting relevant damage sites and suspected defects.
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Total savings / year
As part of TÜV inspections, weld seams and cracks on shipboard container cranes must be inspected. To do this, an on-site inspector captures a large number of images, all of which then need to be reviewed and assessed afterward.
The previous inspection process involved a very high level of manual effort. Every recorded image had to be reviewed individually, even though only a portion of them actually showed critical cracks, relevant welds, or concrete areas of concern.
The goal was to automatically filter the relevant content from the collected images, reliably identify suspected defects, and significantly reduce the number of images requiring manual follow-up review. At the same time, the inspector’s existing workflow needed to remain unchanged.
With Maddox AI, the captured images are automatically pre-sorted. Relevant cracks, welds, and suspected defects are filtered out in a targeted way, so inspectors only need to review the images that are actually relevant for assessment. This preserves the existing process while significantly reducing overall inspection effort.
All captured images had to be reviewed individually after inspection. Suspected defects, cracks, and weld seams had to be manually identified from the full image set.
Only pre-sorted, relevant images are passed on for manual follow-up review. Cracks, weld seams, and suspicious areas are reliably filtered from the captured image set.
The inspector remained fully occupied with follow-up review because no automated pre-sorting was available.
The existing process remains unchanged, but becomes significantly more efficient through intelligent image sorting.
All captured images had to be reviewed individually after inspection. Suspected defects, cracks, and weld seams had to be manually identified from the full image set.
The inspector remained fully occupied with follow-up review because no automated pre-sorting was available.
Only pre-sorted, relevant images are passed on for manual follow-up review. Cracks, weld seams, and suspicious areas are reliably filtered from the captured image set.
The existing process remains unchanged, but becomes significantly more efficient through intelligent image sorting.
In manual follow-up review
With the same assessment workflow
Instead of reviewing the entire image set
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