Using the Sixfab connect platform, you can manage your device remotely and easily accomplish many projects. Using this platform, we will ask you, “How can you make projects, what IoT projects can you develop?”  I will talk about them. It will mainly focus on a deep learning object detection project with OpenCV using Raspberry Pi.

Raspberry Pi is a single-board computer. In other words, microprocessor, RAM, wireless radio and ports are located on a single circuit board.

You can connect Cellular network for remote IoT projects with Sixfab IoT Shield and HAT. Some products include GPS, accelerometer, temperature, humidity, light sensors, relay and so on. You can make an IoT application with the data you receive with the sensors and transmit it wirelessly over the Cellular network.

At this point, Sixfab Connect allows you to make changes to the application on your device, develop new applications, send and receive data. You can easily do this and follow up on Sixfab Connect’s dashboard.

Requirements for the object detection project:

Hardware:

  • Raspberry Pi 3 Model B+
  • Raspberry Pi Camera
  • Sixfab 3G-4G/LTE Base Shield V2
  • Minimum 16 gb SD Cart(Raspbian Buster Full version installed)

Software:

  • First, you must be successfully connected to Sixfab connect. To do this, you can setup with the following command. sudo curl -L installer.sixfab.com | bash
  • Install OpenCV(4.1.0)

1. Make the hardware connection as shown and make sure you are connected to Sixfab Connect.

We’re creating a new widget to display the detected object on Sixfab Connect.

We will use the ID value when sending data.

2. Create new widget.

3. Select the “Basic data” type.

4. Widget Name

5. Widget ID

6. Widget was created successfully.

In order to send the detected object to Sixfab Connect, we need to import the following code into our program.

The code snippet that sends the detected object to Sixfab connect:

In the code snippet we write, we enable us to send the ‘car’ object detected from the camera to the ‘object’ widget.

7. Now we enter the git repository where the files of our project are located.

8. Git repository.

9. We upload and commit the program file and trained data sets from ‘Upload File’ section.

10. In order to publish our changes, we are creating ‘New Release’ in Releases section.

11. Enter the title and content sections and click publish release.

12. New release created.

13. We select ‘Update User Application’ from the ‘Update Configs’ section of Sixfab connect.

14. The project was successfully updated.

15. To run our project, send the following command from the “Commands” tab.

Warning!

Before running this command, make the comments line in the codes 52,95,96,99 and line 100.

16. Object detection

17. The message we sent to Sixfab connect has arrived successfully.

Now let’s make a development in our project. Now let us find the count number of cars it has detected.

We make a change in the code in our project.

18. For this change, locate the real_time_object_detection.py file in the git repository, make the change and commit it.

19. Commit changes

9-10-11-12-13-14. Repeat the steps.

20. Object detection

21. We now send the count number of detected cars to Sixfab connect.

Download all the program codes here.

Thanks.

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