CircuitVision

Project Overview

CircuitVision is an automated optical inspection system for PCB (Printed Circuit Board) boards, utilizing a Raspberry Pi 4 and a KL25 microcontroller. The system, integrated with a CNC machine, automates the process of capturing images of PCB boards and evaluating the placement and presence of components.

Modes of Operation

CircuitVision offers two modes of operation, selectable via a keypad with a menu displayed on an LCD:

1. Manual Configuration and Picture Taking

   • Allows the user to manually control the camera mounted on the CNC machine using a joystick.
   • The user can capture images at any moment, triggering an automatic evaluation of the PCB board based on the selected design.

2. Automatic Inspection of Preconfigured PCB Boards

   • Recommended mode for inspecting multiple PCB boards.
   • The CNC machine automatically captures images of all PCB boards in the test bed, saving and displaying the results on a monitor.

KL25 Microcontroller

The KL25 microcontroller manages the interaction and control of the following hardware components:

• LCD Display
• Joystick
• 3x3 Keypad
• Stepper Motor Driver Control
• UART Communication with Raspberry Pi 4

The KL25 orchestrates the system's operations and communicates with the Raspberry Pi 4 only when PCB evaluation is required.

Raspberry Pi 4

The Raspberry Pi 4 handles image processing and evaluation using OpenCV in C++. The main tasks include:

• Capturing images of the PCB board on the CNC bed.
• Processing and evaluating the images.
• Sending results back to the KL25 microcontroller for display on the LCD or monitor (via HDMI).

Image Evaluation Pipeline

The automatic optical evaluation process is inspired by the following research papers:

• PCB Defect Detection Using OpenCV with Image Subtraction Method by Fa Iq Raihan and Win Ce
• Automatic PCB Inspection Algorithms: A Survey by Madhav Mogonti and Fikret Ercal

The evaluation pipeline involves the following steps:

1. Capture and save the image.
2. Create a mask to filter the white background.
3. Identify the largest contour (the PCB itself).
4. Correct the perspective and resize the image.
5. Apply preprocessing (blurring, edge detection, conversion to a single channel).
6. Perform XOR operation between reference and evaluation images.
7. Remove noise from the XOR result.
8. Read component bounding box coordinates from a CSV file.
9. Generate a box image for each component.
10. Calculate the percentage of lit pixels and compare it to the allowed maximum.
11. Display and save the results.

Preview Images

Below are some temporary reference images from the project: