AIによるハチの検出と追跡

Story

Mixtile is professional IoT hardware solution provider specialized in Linux and Android-based embedded systems.

Mixtile Edge 2 Kit is high-performance ARM single board computer. It comes in variants of 2GB of LPDDR4 DRAM and 16GB eMMC Flash storage, or 4GB of LPDDR4 DRAM and 32GB eMMC Flash storage. This single board computer comes with preinstalled Android 11, and it runs Ubuntu Linux operating system in Android container. It comes with large connectivity options (Bluetooth, 4G/5G Cellular, GPS, and Lora, Zigbee and Z-Wave). For those, you will need module, but it comes with default onboard Wi-Fi connectivity, Gigabit Ethernet Port (RJ45) and Serial Port (RS-485). Because it comes with RS-485 port, which is industrial standard, and it comes within a strong metal case, it seems to me that it can be really used in industrial projects. I used official Raspberry Pi 5 power supply in order to power up my Mixtile Edge 2 Kit.

So, an idea came to me why not to use it in agriculture, bee detection, which can be essential for health and survival of bees.

This project will cover setting up Mixtile Edge 2 Kit, and custom photo dataset form video in order to train custom YOLOv5 bee detection model.

YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data.

I gathered data from video and trained model on my PC.

To train a model, I used python and typed in command line:

python train.py –img 640 –batch 16 –epochs 3 –data coco128.yaml –weights best.pt

My training results are summarized in the following table:

From this table you can see that images are divided into 4 detection classes:

• Bee
• Drone
• Pollenbee
• Queen

I used 100 images for each class (400 in total) and 1000 labels total.

Example for each class is summarized in a table below:

Bee Classes

Getting started

First, I will write about software part of the project, and later on steps of starting the recognition.

1. What is YOLOv5?

If you have been in the field of machine learning and deep learning for some time now, there is a high chance that you have already heard about YOLO. YOLO is short for You Only Look Once. It is a family of single-stage deep learning-based object detectors. It was written using Python language, and the framework used is PyTorch.

To ease control, I connected usb mouse to the one of three Mixtile Edge 2 Kit USB3 port. I used Ubuntu Linux for this project. Ubuntu on container is installed in Android system of Mixtile Edge 2 Kit by default. When you boot Mixtile Edge 2 Kit, you get Android OS. Since I wanted to access Edge 2 Kit remotely, and get easier control, I installed droidVNC server from this link:

https://f-droid.org/en/packages/net.christianbeier.droidvnc_ng/.

It is an Android VNC server using Android 5+ APIs. It does not require root access.

I started the VNC server, connected with VNC Viewer and I got the following Android 11 screen:

アンドロイド11

After that, I installed SimpleSSHD from this link:

https://f-droid.org/en/packages/org.galexander.sshd/

SimpleSSHD is a SSH server Android app, based on Dropbear.It allows user access (user ssh) or full root access (by setting the login shell to /system/xbin/su) (if root is allowed).

After I installed SSH server, I connected to it via putty SSH terminal. Username and Password are root/root.

Com.hubware.ubuntu is ubuntu on a container and we are connected to it immidiately.

Now we are going to install required software.

First, you will need to upgrade Ubuntu by typing in the command: apt-get upgrade.

Second, I installed python by typing: apt-get install python.

You will also need pip, the package installer for Python.

Last, but not less important: If you want to safely turn off your Mixtile Edge 2 Kit, I recommend you to install Shutdown (no Root) application: https://play.google.com/store/apps/details?id=com.samiadom.Shutdown&hl=en.