arXiv Computer Vision
Beyond CLIP: Calibrated (Few-Shot) Defect Detection With System One Decision Models
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Trustworthy Visual Quality Inspection under Data Scarcity in Manufacturing
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Zero-Shot Learning in Industrial Scenarios: New Large-Scale Benchmark, Challenges and Baseline
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Answer-Conditioned Chain-of-Thought Distillation for Few-Shot Industrial Vision with Small VLMs
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InspectorGPT: A Comparative Reasoning Enhanced VLM for Comprehensive Industrial Anomaly Detection
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Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines
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Region-Aware Multi-View Inspection Using Deep Learning and Domain-Informed Decision Fusion
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.