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arXiv Machine Learning By Srivishnu Ethirajulu Krishnaraj, Rishikesh R. Chopade

Region-Aware Multi-View Inspection Using Deep Learning and Domain-Informed Decision Fusion

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The paper introduces a region‑aware dual‑model inspection framework that uses instance segmentation for seat‑region defects and image‑level classification for shaft‑region anomalies, followed by a deterministic decision fusion layer that applies industrial SOPs to produce a final component‑level decision. In experiments on 380 automotive fuel injector needles, the fusion layer maintained 100% recall of non‑conforming parts while reducing the false‑rejection rate from 57.6% to 10.9%, an 81.1% drop in false positives. The study shows that formalizing acceptance criteria can translate sensitive visual detections into practical, traceable decisions for precision component inspection.

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 Machine Learning.

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