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arXiv Computer Vision By Simone Garbin, Leonardo Venturoso, Marco Todescato

Automatic weld seam segmentation for industrial quality control: a comparison of RGB and polarimetric imaging with CNN and transformer architectures

Read the original on arXiv Computer Vision →

The study evaluates automatic weld seam segmentation using RGB and polarimetric images, comparing convolutional neural networks (CNNs) and transformer architectures. In controlled RGB settings, CNNs achieve a mean mask mAP50 of up to 0.87, but performance drops significantly under uncontrolled conditions. Polarimetric imaging, combined with geometric augmentation, reaches a mean mask mAP50 of up to 093 even in uncontrolled settings, and transformer models, especially RF‑DETR, maintain high accuracy under viewpoint shifts while CNNs fail.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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