🔎 Overview

Accurate PCB placement is critical in wave soldering lines, but manual alignment inspection is time-consuming and prone to error. This project implements an automated PCB alignment detection system using a Raspberry Pi and computer vision algorithms. The first implementation applied ORB feature matching to compare incoming PCBs with a reference design, achieving strong robustness against rotation, scaling, and partial occlusion. A Tkinter GUI enables region-of-interest selection, while the system triggers conveyor continuation or buzzer alerts based on alignment status.

Currently, the project is under further development to build a more robust detection pipeline that can handle varying lighting conditions and real-world factory noise, ensuring consistent detection accuracy in production environments.

🔗 GitHub Repository: https://github.com/RishobanK/pcb-alignment-detection-opencv

✨ Features

• Real-time ORB feature matching
• Robust against rotation, scaling, occlusion
• Tkinter GUI for region-of-interest selection
• Automated conveyor/buzzer triggering

⚙️ System Architecture

🛠 Workflow

1. Camera captures PCB image.
2. ORB features matched with reference PCB.
3. Tkinter GUI allows ROI selection.
4. Conveyor continues or buzzer triggers depending on alignment.

🎥 Usage

• Scenario: PCB placed incorrectly → system detects mismatch → buzzer alert → conveyor stops.

📊 Results

• High robustness against distortion.