Real-Time PCB Defect Detection with Computer Vision

Printed Circuit Board (PCB) manufacturing requires precise defect detection to prevent issues like open circuits or spurious copper, which can lead to failures or safety hazards. A real-time PCB defect detection system was developed using computer vision, employing a Roboflow-trained object detection model integrated with a Python GUI for user-friendly results display. The system uses Roboflow Workflows to classify defects and indicates whether a PCB is a "guaranteed fail" or requires further human inspection, enhancing efficiency in manufacturing environments. The process involves collecting high-quality images of PCBs, training a model using Roboflow’s RF-DETR architecture, and setting up a real-time inference workflow with custom decision-making logic. The Python application displays detection results in a modern GUI, simplifying the inspection process. This approach automates quality control, reducing errors and manual inspection time, thus ensuring reliable PCBs for electronics production. The system's development details, including model training and GUI construction, are explained, with resources available for replication.