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 in an image.

In the previous video tutorial, 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 field of view corresponds to a specific object—for instance, 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. OpenMV’s main control MCU is the STM32H7; recently, we have 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 demonstration for traffic sign detection in real-road environments, detecting traffic signs on our actual roadssuch as no honking, no parking, and a speed limit 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 subject to numerous interferences. 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 required 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 different digits, various fruits, distinct markers, diverse components, or even any specific irregular object, using our video tutorials; this enables detection of the target’s count, coordinates, and object class name.

Note:This video tutorial covers the neural network-based keypoint detection feature, which is compatible not only with the OpenMV4 H7 Plus but also with the OpenMV4 H7. Since the trained FOMO keypoint detection model is compact—exhibiting excellent performance while occupying only several tens of kilobytes—it can run efficiently on the OpenMV4 H7 despite its smaller RAM capacity compared to the Plus version; accordingly, a slightly smaller model can be trained for deployment on the OpenMV4.

Below is a brief introduction to the principle:

The primary design decision behind FOMO object detection on OpenMV is based on the idea that many object detection tasks do not actually require the size of objects, but only their locations within an image. Once we know the positions of objects, subsequent operations can be performed—such as using OpenMV to control a robot car to move toward detected targets, enabling drones to land at designated locations, or controlling 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 very simple and flexible. It first divides the image into patches, 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, this results in a grid of 40×40 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 image datasets, annotating objects, training, testing the model, and deployment.

* Collecting the image dataset: Capturing images of specific objects in the actual environment where recognition will be performed. Images are collected in the same environment where recognition will occur, 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 detecting object locations and names.

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

* Test Model: We evaluate the trained model; if the performance is unsatisfactory, we accordingly 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 device.

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 choose “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 details, 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 the actual environment where detection will be performed. We have prepared four to five hundred images of traffic signs, including “No Parking”, “No Honking”, and “Speed Limit 80 km/h”, all collected from real roads. This dataset is available for download from our GitHub repository and tutorial website.

02. Annotation Target

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, where three colours represent three distinct markers.

Configure the target detection training parameters. The default number of training epochs is 60, the learning rate is 0.001, and the validation set proportion is 20%. Enable data augmentation. For transfer learning, you can select 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 neural network model obtained after training has an F1 Score of 91.2%, and we can save the current version.

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

04. Deploying 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

Run the model on OpenMV:

Connect the OpenMV Plus, store the three trained files in 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. Since 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 size:

1. 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!