OpenMV Self-Training Neural Network Target 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. This enables real-time determination of whether an image within the OpenMV field of view corresponds to a specific object—for example, whether a face is wearing a mask—but it cannot output the positional 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 MCU of OpenMV is the STM32H7. We have now added a new feature to SingTown that enables target detection–like tasks to run on microcontrollers, delivering outstanding performance.

Today we will explainTraining demonstration of 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—particularly 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, then trained a model on the EdgeImpulse online platform—a partner of OpenMV—by manually labelling 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 ten 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, specific components, or even any particular 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-based object point detection feature, which is compatible not only with the OpenMV4 H7 Plus but also with the OpenMV4 H7. The trained FOMO object point detection model is compact, delivers excellent performance, and occupies only tens of kilobytes; 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 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 an image. Once the positions of objects are known, subsequent operations can be performed—for example, OpenMV controlling a vehicle to move towards a detected target, enabling a drone to land at a designated location, or controlling a robotic arm to grasp a specific target object.

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

The underlying principle of the FOMO model is exceptionally simple and flexible. It first divides an image into patches, each measuring 8×8 pixels. For a 96×96-resolution image, this yields a 12×12 grid of cells; for a 360×360 image, it yields a 40×40 grid. Image classification is then performed independently on each cell.

FOMO performs significantly better than YOLO V5 or MobileNet SSD on large numbers 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 size—for example, when the markers to be identified are approximately the same size, rather than comprising one extremely large object and one extremely small object.

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

* Collect image datasets: Capture images of specific objects in the actual environments where they will be identified. Images are collected in the same environments where identification 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 target to be detected in our dataset and associate each box with the corresponding target name to facilitate subsequent training, target localisation, and target naming.

* 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 performance is unsatisfactory, we accordingly expand the training dataset or adjust the training parameters and continue training until a satisfactory model is achieved.

*Deployment: We simply place the trained model file onto the OpenMV’s built-in USB drive and run it.

We now demonstrate the specific procedure below:

01. Collect and upload the image dataset

1. 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 an 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 in 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 for mask detection.

2. Images downloaded from the internet or captured using a mobile phone. Preferably, these images should match your actual detection environment. We have prepared a dataset comprising approximately four to five hundred images of traffic signs, including “No Parking”, “No Honking”, and “Speed Limit 80 km/h”. Each image was captured on real roads. This dataset is available for download from our GitHub repository and tutorial website.

02. Annotation Target

Approximately 100 images are uploaded for each category; once the images have been uploaded, image annotation must be performed to mark both the location and the category 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 colours representing 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, 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 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. 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

Run 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 results will appear 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:

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!