YoloV3

Laptop Test:

To test the code on a computer, you should have previously followed the instructions at https://github.com/altaga/DBSE-monitor#laptop-test.

You must download the model from the link below and put it inside the weights folder for the code to work

https://pjreddie.com/media/files/yolov3.weights

Open the Notebook.ipynb file in a jupyter notebook or the notebook.py file in any IDE that supports python.

NOTE: It is highly recommended to use jupyter notebook to run this code due to its ease of use.

Model:

In this case we have used an existing model called YoloV3 used for object detection in addition to its own algorithm to detect the distance of objects with a simple camera.

The model creator page: https://pjreddie.com/darknet/yolo/

How does it work:

The blind Spot monitor uses the following libraries:

• OpenCV:
  • Image processing
  • CDNN implementation.
    • The weights of the CDNN were obtained from YoloV3 and imported by PyTorch.

Only for Jetson Nano:

• MQTT with Mosquitto:
  • Communication with the ESP32.

In this algorithm we use the detection of objects using PyTorch, YoloV3 and OpenCV which allows the use of CDNN.

In this case, I created an algorithm to calculate the approximate distance at which an object is located using YOLOV3, the algorithm is as follows.

We calculate the aperture and distance of our camera, in my case it is 19 centimeters in aperture at a distance of 16 cm.

It means that an object 19 cm wide can be at least 16 cm away from the camera so that it can fully capture it, in this case the calculation of the distance of any object becomes a problem of similar triangles.

In this case, since our model creates boxes where it encloses our detected objects, we will use that data to obtain the apparent width of the object, in this case the approximate width will be stored in a variable during the calculation.

If we combine both terms we get the following equation.

In this case for the objects that we are going to detect, we approximate the following widths in centimeters.

{
    person:70,
    car:220,
    motorcycle:120,
    dogs:50 
}

Here is an example of its operation with the same image.

Testing the algorithm with the camera.

Once having this distance, we will filter all distances that are greater than 2 meters, this being a safe distance to the car at the blind spot.

In turn, we will determine in which of the 2 blind spots the object is, right or left. Depending on the object and the side, the information will be sent via Mosquitto MQTT to the display. For example a car on the left side: