AI-powered assistant for EasyEDA — generate schematics from natural language, browse LCSC components, design PCBs with custom DRC configurations, and get interactive circuit design help.

The goal of this project is to apply deep learning methods and image processing techniques to automatically identify defects such as incorrect soldering, missing components, or faulty connections, improving the quality inspection process for PCBs

Intelligent PCB anomaly detection system based on OpenCV. Features quadrant-aware direction adaptive cutting, pad masking constraints, and structural affinity scoring for precise coordinate generation. [Under Continuous Optimization]

Automated PCB AOI defect detection using YOLOv8 and ONNX Runtime for real-time industrial inspection.

Production PCB defect detection: 99.5% mAP, 3x inference speedup (ONNX+INT8), FastAPI deployment. YOLOv8 + complete MLOps pipeline.

AI-powered quality inspection system for Printed Circuit Boards (PCBs). Uses U-Net (ResNet34) with PyTorch for anomaly detection/segmentation, featuring an interactive Gradio UI and live history tracking.

Agentic PCB defect inspection using Amazon Nova on AWS Bedrock — 7-step MCP pipeline with autonomous tool-calling (quarantine, work orders, knowledge graph)

Deep learning pipeline for automated PCB defect inspection using YOLOv8, synthetic augmentation, and ONNX export. Reaches 0.983 mAP@0.5 on DeepPCB

PyQt GUI for Automated Optical Inspection: PCB super-resolution, Faster R-CNN component counting, image export, and PDF reports.