This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.
An AI-driven industrial anomaly detection system. Utilizes a fine-tuned YOLOv8 model trained on the MVTec AD dataset to inspect surfaces, sending real-time stop/start signals directly to Siemens S7 PLCs via Snap7.
Self-supervised learning for low-annotation surface defect detection and inspection
Unsupervised industrial surface defect detection using a one-class Deep Feature Memory Bank with ResNet-18, achieving 1.0000 ROC-AUC and 100% defect recall.
C++17 + OpenCV rule-based surface defect inspection engine. PLC/Camera/Robot simulators + real Modbus TCP adapter, Streamlit + C# WinForms UIs, GoogleTest, GitHub Actions CI.
SNY Septa Line Inspection: seven-camera surface inspection for a coated technical knit. Built with FluentQt.
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