Industrial Vision Systems and Defect Detection

Industrial vision systems use cameras, optics, and computational algorithms to automatically inspect manufactured goods for flaws that human inspectors might miss or that appear too rapidly on a production line to catch manually. In fabric and semiconductor manufacturing, researchers apply techniques such as Gabor filters for texture analysis and deep learning models for wafer map defect classification to identify surface anomalies with high speed and reliability. The practical stakes are significant: a single undetected defect in a semiconductor wafer can render an entire chip non-functional, while fabric flaws drive up material waste and rework costs at scale. Active research questions include how to build robust detection models that generalize across novel defect types with limited labeled training data, and how to integrate these systems with real-time process control so that detected anomalies can trigger immediate manufacturing corrections rather than simply flagging finished products for rejection.

Works

102,614

Total citations

744,246

Keywords

Fabric Defect DetectionMachine VisionTexture AnalysisSemiconductor ManufacturingDeep LearningWafer Map Defect Classification