OpenMV Self-Training Neural Network for Target Point Detection – Traffic Sign Recognition in Real-Road Environments

Artificial intelligence has two very common tasks in the visual domain: classification and object detection.

Category:Determine which category the image belongs to, for example, whether it is a cat or a dog.

Object Detection:Used to locate the positions, quantities, and dimensions of multiple distinct objects within an image.

In previous video tutorials, we explained how to use the OpenMV4 Plus to train neural networks online for classifying different objects. It can determine in real time whether an image within the OpenMV’s field of view contains a specific object—for example, whether a face is wearing a mask—but it cannot output the coordinates of faces or masks, nor the number of faces.

Target detection is significantly more complex and computationally intensive than classification, making it rare in the microcontroller domain. The main control MCU of OpenMV is the STM32H7; we have now added a new feature to OpenMV that enables target detection–like tasks to run on microcontrollers, delivering outstanding performance.

Today, we will explain to youTraining demonstrates traffic sign detection in real-road environments, detecting traffic signs on our actual roadssuch as no-horn zones, no-parking zones, and speed limits of 80 km/h, rather than a laboratory environment free from interference.

You can view the entire project content, including all code and models, at the following address: https://book.openmv.cc/project/traffic-sign.html

It is widely known that real-world environments—especially road environments—are highly complex and contain numerous interfering factors. We captured images of traffic signs in real-world environments using devices such as smartphones, trained the model on EdgeImpulse—the online platform of our OpenMV partner—and manually annotated approximately 270 images. The training process took only ten minutes, yielding a model with an F1 score accuracy of 92%. When deployed on OpenMV, the model achieves a frame rate of 10 frames per second, delivering both smooth performance and high accuracy.

Users can train the system to detect any target of interest—such as various numerals, different fruits, distinct markers, diverse components, or even any specific irregular object—by following our video tutorials, thereby enabling detection of the target’s count, coordinates, and object class name.

Note:This video tutorial covers the neural network keypoint detection feature, which is compatible with both the OpenMV4 H7 Plus and the OpenMV4 H7. The trained FOMO keypoint detection model is compact—only tens of kilobytes—and delivers excellent performance; thus, even though the OpenMV4 H7 has less RAM than the Plus version, we can train a slightly smaller model to run on the OpenMV4.

Below is a brief introduction to the principle:

The primary design decision behind FOMO object point detection on OpenMV is based on the idea that many object detection tasks do not actually require the size of objects, but only their positions within the image. Once the positions of objects are known, subsequent operations can be performed—such as controlling a vehicle to move toward detected targets using OpenMV, enabling drones to land at designated locations, or commanding robotic arms to grasp specific target objects.

The FOMO model is a model specially designed by EdgeImpulse for microcontroller environments; unlike conventional object detection, it can only detect the positions and count of multiple objects, but cannot determine their exact dimensions.

The underlying principle of the FOMO model is exceptionally simple and flexible. It first divides the image into blocks, each measuring 8×8 pixels. For an image with a resolution of 96×96, this results in a grid of 12×12 cells; for an image with a resolution of 360×360, it yields a grid of 45×45 cells. Image classification is then performed independently on each cell.

FOMO performs significantly better than YOLO V5 or MobileNet SSD on a large number of small objects. FOMO object point detection is 30 times faster than MobileNet SSD.

FOMO performs better when the objects to be detected are of similar sizes—for instance, when the markers to be identified are roughly uniform in size, rather than varying significantly between very large and very small objects.

OpenMV uses EdgeImpulse to train neural network object detection models online, primarily involving the following steps: collecting and uploading the image dataset, labeling objects, training, testing the model, and deployment.

* Collecting the image dataset: Capture images of specific objects in the actual environment where recognition will be performed. Images are collected in the same environment where recognition will take place, ensuring that the actual training environment closely matches the actual detection environment, thereby achieving better results.

* Annotation Target: Draw bounding boxes around each object to be detected in our dataset and associate each box with the corresponding object name to facilitate subsequent training for object localization and classification.

* Training the Model: After annotation is completed, train the model using the convolutional neural network parameters designed by us.

* Test Model: We conduct testing using the trained model; if the performance is unsatisfactory, we correspondingly expand the training dataset or adjust the training parameters and continue training until an acceptable model is achieved.

*Deployment: We can run the trained model file directly from the built-in USB drive of the OpenMV.

Below is a demonstration of the specific process:

01. Collect and upload the image dataset

I. First, log in to the website edgeimpulse.com, select “Log In”, enter the name of the new project, and select “Images” → “Classify multiple objects”

II. Select the image to upload. There are several ways to obtain the image dataset:

1. Capture images in real time using the OpenMV IDE. Use the “Dataset Editor” tool within the OpenMV IDE to create a new dataset and capture images in real time; for detailed instructions, refer to our previous video tutorial on object classification and mask detection.

2. Images downloaded from the internet or captured using a mobile phone; preferably matching your actual detection environment. We have prepared approximately four to five hundred images of traffic signs, including “No Parking,” “No Honking,” and “Speed Limit 80 km/h,” all collected from real road conditions. This dataset is available for download on our GitHub repository and tutorial website.

02. Labeling Objectives

Upload approximately 100 images for each category; after uploading the images, proceed with image annotation to mark the positions and categories of the objects to be identified.

03. Train the Model

Configure the training parameters, change the resolution to 128×128, and select all default parameter settings; then click Save.

Generate features, with three colors representing three distinct markers.

Configure the object detection training parameters. The default number of training epochs is 60, the learning rate is 0.001, and the validation set ratio is 20%. Enable data augmentation. For transfer learning, select either the default MobileNetV2 0.35 or MobileNetV2 0.1. (Note: SSD cannot be used, as it is not supported on OpenMV.)

Select Start Training. The trained neural network model achieves an F1 Score of 91.2%; we can save the current version.

Select Storage, enter your description, and then select Save.

04. Deploy the Model

Export the trained model file. Only the library needs to be exported; select OpenMV and click Build to begin deploying and exporting the model.

It will automatically download the exported model, which in this case is the model trained on our 300 images, thereby completing the entire training process.

05. Run the Model

Running the model on OpenMV:

Connect the OpenMV Plus, save the three trained files to the OpenMV’s built-in USB drive, and open the ei_object_detection.py file in the OpenMV IDE. Click Run, and the execution results will be displayed in the serial terminal.

This feature is also compatible with the OpenMV4. As the OpenMV4 does not have external SDRAM and has less memory, we can reduce the model size to enable it to run on the OpenMV4.

There are two methods to reduce the model’s size:

I. Reduce the training resolution from 128×128 to 96×96.

II. Modify the transfer learning model used by changing MobileNetV2 0.35 to MobileNetV2 0.1.

Finally, place the trained new file into the OpenMV4 and run it.

The above is our target point detection tutorial. We look forward to your results!