How to best suit your AI vision module?

Today I will introduce some different types of vision modules to help users better choose the solution that suits them.

About the visual module:

The visual module is a device that integrates a processing chip and a camera, and has rich visual processing capabilities. It can directly output the results of visual recognition and transmit these results to other main controllers through IIC or serial ports.

Users can quickly call visual recognition results without having to deeply understand the underlying technology of image processing, which greatly reduces debugging time and technical difficulty.

Ordinary cameras need to be used with embedded controllers such as Raspberry Pi or Jetson, and users also need to implement image recognition algorithms themselves, which not only increases the hardware cost of the project, but also increases the complexity of software development..

There are three common types of visual modules: OpenMV visual module, K210 visual module, ESP32 visual camera module.

I will introduce these camera module respective characteristics and applicable scenarios.

OpenMV visual module

The OpenMV camera module is one of the most classic camera modules, which has won wide recognition for its powerful image processing capabilities and ease of use. It allows users to write embedded machine vision programs using MicroPython, which is very suitable for rapid prototyping, teaching and micro projects.

The latest generation of OpenMV4 H7 Plus is equipped with an STM32H7 processor and a 5MP camera, which can realize multiple functions such as color recognition, face detection, QR code detection/decoding, circle detection, rectangle detection, template matching, etc.

However, with the development of technology, the design of OpenMV based on single-chip microcomputer has gradually shown its shortcomings in computing power, it is difficult to meet the needs of higher performance projects. 

Advantages:

High ease of use: Allows users to program using MicroPython, making it suitable for rapid prototyping, teaching, and small projects.

Abundant example resources: Offers a large collection of open-source examples and tutorials, making it easy for beginners to get started.

High camera resolution: The latest OpenMV4 H7 Plus is equipped with a 5MP camera, suitable for applications requiring high-quality images.

Disadvantages:

Low performance: Its microcontroller-based design shows limitations in computational power, making it difficult to meet the demands of high-performance tasks, especially for processing complex neural network models.

Lack of display: Without a built-in display, it cannot directly show visual recognition results, increasing debugging difficulty.

High cost: The relatively high price may not be suitable for budget-constrained projects.

Limited expandability: When faced with complex tasks, the scalability of its hardware and software is relatively limited.

K210 visual module

The K210 vision module not only inherits all the advantages of OpenMV, but also achieves significant improvements in performance.

On board dual-core 64-bit processor has a built-in neural network accelerator, and the total computing power can reach 1TOPS, allowing the K210 to efficiently run complex neural network models.

It is especially suitable for vision applications with high performance requirements, such as color recognition, face recognition, number recognition, license plate recognition, autonomous learning classification, etc.

In addition, the K210 is equipped with a 2MP camera and a 2.0-inch LCD display, which can display visual recognition results in real time in the dynamic environment of the robot or car.

Advantages:

High-performance processor: Onboard dual-core 64-bit processor, built-in neural network accelerator, total computing power can reach 1TOPS, far exceeding similar products.

Rich application scenarios: It can realize complex tasks such as color recognition, face recognition, digital recognition, license plate recognition, self-learning classification, etc., and adapt to various visual application needs.

Built-in LCD display: Equipped with a 2.0-inch LCD display, it can display visual recognition results in real time in the dynamic environment of the robot or car, which is convenient for debugging and display.

Extensive community support: It provides a large number of cases and tutorials to facilitate users to get started quickly.

Disadvantages:

Low-resolution camera: Despite its powerful performance, the camera has only 2MP, which may not perform well in some applications that require high-resolution images.

ESP32 visual module

The ESP32 series of products were originally known for their IoT and wireless communication functions, but after the release of the ESP32-S3 high-performance chip, ESP also officially opened a series of visual module applications.

Although the ESP32 visual module is not as good as the K210 in performance, it is stronger than the OpenMV module. It is also equipped with a deep neural network computing chip, which can handle more complex image processing tasks.

In addition to its powerful visual processing capabilities, ESP32 also retains its inherent advantages in the field of IoT. It is equipped with a 2MP camera, which can easily realize remote video streaming, effectively making up for the lack of screen display.

However, compared with the K210 and OpenMV modules, ESP32 is slightly lacking in actual application scenarios and the number of cases.

Currently, ESP32 vision modules are mainly divided into two categories:

One is that combines image transmission and AI vision processing.

Another is an AI vision module based on ROS (Robot Operating System). It is more suitable for ROS developers to build remote vision solutions.

Advantages:

Better wireless communication capabilities: With excellent Wi-Fi and Bluetooth functions, it can easily achieve remote video streaming, which is particularly suitable for scenarios that require wireless communication.

Better cost-effectiveness: Compared with OpenMV modules and K210, ESP32 has achieved a better balance between performance and price, and is suitable for projects with medium performance requirements.

Built-in deep neural network computing chip: Supports lightweight neural network models, suitable for some simple AI vision applications.

Compact size: Small size and light weight, suitable for portable or space-constrained projects.

Disadvantages:

Average performance: It is stronger than the OpenMV module, but not as good as the K210, especially when facing high-precision visual tasks.

Lack of screen display: ESP32 itself does not have a built-in display and cannot intuitively display visual recognition results.

Relatively complex development environment: The development environment and tool chain of ESP32 are relatively complex and may require more configuration and debugging work.

Independent traditional camera+embedded main control board

In some electronic design competition projects that require extremely high computing power and flexibility, using independent cameras with high-performance embedded controllers (such as Raspberry Pi, NVIDIA Jetson, etc.) is also a common solution.

Although this method can provide the strongest computing power and the most flexible development environment, it also means higher costs and energy consumption, as well as a more complex system construction process.

Advantages:

Powerful computing power: Equipped with powerful multi-core CPU and GPU, it can handle complex image processing tasks and deep learning models.

Flexible development environment: Supports multiple programming languages, including Python, C++, CUDA, etc.

Better compatibility: The main control board is usually equipped with multiple interfaces and supports multiple types of cameras.

Disadvantages:

Expensive: Especially the NVIDIA Jetson series, its price is much higher than that of ordinary microcontrollers or microcontrollers.

High power consumption: High power consumption may cause problems such as heating and battery life.

Large size: High-performance main control and its peripherals are usually large in size and are not suitable for projects with limited space.

High development difficulty: More problems may be encountered during debugging, such as hardware compatibility, software conflicts, etc., which is not friendly to beginners.

Summary:

If you are looking for ease of use and quick start, and the project requirements are relatively simple, the OpenMV module and the K210 vision module are both good choices, especially for teaching and small projects.

If you need higher performance and accuracy, and are willing to invest more time and resources in development, the K210 is one of the most powerful vision modules on the market, especially suitable for complex AI vision applications.

If you need wireless communication capabilities and want more expansion in the field of the Internet of Things, ESP32 is a cost-effective choice for projects with medium performance requirements.

If you need to handle complex AI tasks and the project budget is sufficient, high-performance embedded master control (such as Raspberry Pi, Jetson) is the best choice, especially for autonomous driving, drones, industrial robots and other application scenarios with extremely high computing power and real-time requirements.