Machine Vision with Arduino Modules and Low Cost

  • Configure affordable camera modules for machine vision in Arduino projects.
  • Optimizes hardware and software to manage image data efficiently.
  • Deploy tools like TensorFlow Lite Micro for advanced machine learning projects.

artificial vision

Machine vision is a growing field, and thanks to tools like Arduino and affordable camera modules, it is now possible to implement innovative projects without investing large sums of money. This article will explore the possibilities of working with artificial vision in Arduino projects using low cost modules, taking advantage of libraries and techniques to optimize results.

If you are a technology enthusiast or developer looking for new ways to experiment, integrate a camera opens up a world of possibilities for your Arduino project. Here you will find a detailed guide that brings together everything you need about machine vision with accessible modules.

Hardware Configuration for Machine Vision Projects

vision bundle

To work with artificial vision In Arduino, it is essential to start with the correct configuration hardware. The OV7670 camera module is one of the most popular choices due to its low cost and versatility. This module can be connected to boards such as Arduino Nano 33 BLE Sense. The main advantage of this camera is its support for VGA resolutions (640 x 480) and its integration with tools such as TensorFlow Lite Micro for machine learning projects.

For initial installation, you will need:

  • A compatible motherboard, such as Arduino Nano 33 BLE Sense.
  • An OV7670 camera module.
  • Cables to make the connections correctly.
  • A suitable power supply.

The biggest challenge lies in connecting the different module pins to the motherboard. Here, precision is key, as incorrect connection could lead to failures. Using adhesive tape to secure cables is a simple but effective solution.

Software Configuration

Once the hardware is ready, the next step is to prepare the development environmentThe Arduino IDE is the most common tool for compiling and uploading programs to the board. From the library manager, you can install the Arduino_OVD767x library, designed specifically to work with this camera.

Steps to configure the software:

  • Download and install the Arduino IDE.
  • Open the library manager from 'Tools'.
  • Find the Arduino_OV767x library and install it.

Once installed, you can test the system using the included CameraCaptureRawBytes example. During this stage, the module will start outputting raw binary images over the serial port. If everything is set up correctly, you should be able to see a test pattern before proceeding to live imaging.

Optimization for Computer Vision with TinyML

In more advanced applications, such as those based on automatic learning, it is crucial to optimize the system to handle large amounts of data. For example, VGA images consume about 300 KB memory, which exceeds the capacity of boards such as the Nano 33 BLE Sense.

To solve this problem, the OV7670 module allows working with lower resolutions such as QVGA (320×240) or QCIF (176×144), adjusting the data before sending it to the Arduino. You can also choose different color formats such as RGB565 or YUV422, depending on the needs of the project. These formats define how color values ​​are stored within each pixel to optimize memory usage.

Some projects even reduce the resolution further by applying techniques of down sampling, strategically removing pixels or interpolating values ​​to maintain visual quality. This step is essential if you work with deep learning models, such as TensorFlow models, which tend to require smaller image sizes for a efficient training.

Practical Uses: Object Recognition with Pixy2

Another interesting module is Pixy2, which easily connects to Arduino boards to implement object recognition. This device is capable of identifying up to seven objects in real time and combining its functionality with OLED displays or audio players.

Pixy2 also stands out for its ability to detect lines and generate small barcodes, specifically designed for robots that follow marked paths. To configure it, you can use the software Pixymon, designing color signatures for the different objects that the system must identify.

Process Optimization for Artificial Vision

Work with artificial vision on Arduino requires optimizing both hardware and software. For example, functions like digitalRead and digitalWrite can slow down data capture if not used carefully. Instead, managing GPIO ports directly using more specific commands can significantly speed up the process.

Some key tips to optimize performance:

  • Use lower resolutions like QCIF for applications that don't require high quality.
  • Simplify code by eliminating unnecessary loops.
  • Consider using SIMD techniques for faster operations on compatible CPUs.

In the case of the OV7670 module, recent improvements in the Arduino_OV767x library have allowed the transfer images to memory at impressive speeds. For example, it was possible to reduce the data capture time of 1500 ms only 393 ms for QCIF images.

Leveraging TensorFlow Lite Micro

For those looking to take their projects to the next level, TensorFlow Lite Micro offers specialized tools for working with Artificial Intelligence on microcontrollers. This optimized library can detect advanced patterns such as facial recognition or gesture detection, using pre-trained models tuned for resource-constrained devices.

This environment also benefits from recent optimizations such as CMSIS-NN, which dramatically reduces inference time by leveraging processor-specific instructions such as SIMD. This makes machine learning applications on Arduino much faster and more efficient.

Navigating the world of machine vision with Arduino is an enriching experience. From the initial setup of low-cost cameras to the implementation of machine learning algorithms, the possibilities are virtually limitless. With a creative approach and the right resources, you can explore areas such as object recognition, line tracking or even advanced real-time artificial intelligence projects.


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