Transcend ECM300 Jetson Object Detection Demo

This project demonstrates real-time Object Detection on the NVIDIA Jetson ORIN Nano platform, using the Transcend ECM300 embedded camera module.

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Table of Contents

• Hardware Requirements
• Install NVIDIA Jetson Nano OS
• Download Virtual Environment
• Connect the ECM300 Camera
• Quicky Run Object Detect
• Set Up Virtual Environment
• Run Object Detect
• Third-Party Licenses

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Hardware Requirements

1. Supported Platforms

   • NVIDIA Jetson ORIN Nano

2. Supported Cameras

   • Transcend ECM300

3. Recommended SD Card

   • A microSD card with at least 128 GB capacity and UHS-1 speed class.

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Install NVIDIA Jetson Nano OS

1. Download the Jetson Nano Developer Kit SD Card Image from https://s3.ap-northeast-1.amazonaws.com/test.storejetcloud.com/ECM300+Image/ecm300_jetpack.zip
2. Write the image to the target microSD card using a graphical tool such as Balena Etcher.
3. Once the image has been written, insert the microSD card into the Jetson’s native slot and power on the device.
4. The default login username and password are both “user”.

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Download Virtual Environment

1. Download the Virtual Environment from https://s3.ap-northeast-1.amazonaws.com/test.storejetcloud.com/ECM300+Image/object_detect_demo_venv.zip
2. Unzip the downloaded ZIP file.
3. Copy the extracted object_detect_demo_venv folder to the desktop.

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Connect the ECM300 Camera to Jetson ORIN Nano

1. Insert the camera module as follows:

• ECM300, insert into the CAM1 port.

2. Open a terminal and run:

   cd /opt/nvidia/jetson-io
   sudo python jetson-io.py

3. In the configuration menu, select the following options in order:

   • “Configure Jetson 24pin CSI Connector”
   • “Configure for compatible hardware”
   • Select “Camera IMX675-C”
   • “Save pin changes”
   • “Save and reboot to recognize pins”

4. After rebooting, verify that the camera is connected:

   ls /dev/video*

5. If /dev/video0 appears, the ECM300 has been successfully connected.

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Quicky Run Object Detect

1. Run the Script run_webcam.sh

cd Desktop/object_detect_demo_venv
./run_webcam.sh

2. Choose the Detection Classes

When prompted, select the desired object classes, or enter 'd' to use the default list.

Example: If you want to detect Person, Glasses, Tie, and Monitor/TV, enter: 233,136,325,201

Press 'q' to exit fullscreen mode.

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Set Up a Virtual Environment

cd Desktop/object_detect_demo_venv
source .venv/bin/activate

To deactivate the virtual environment:

deactivate

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Run Object Detect

1.Set Up a Virtual Camera

sudo modprobe v4l2loopback devices=1 video_nr=10 card_label="VirtualCam" exclusive_caps=1

2.Start the GStreamer Pipeline

echo "Starting GStreamer camera pipeline in the background..."
gst-launch-1.0 nvarguscamerasrc ! 'video/x-raw(memory:NVMM),width=1920,height=1080,format=NV12,framerate=30/1' ! nvvidconv ! 'video/x-raw, format=I420' ! videoconvert ! 'video/x-raw, format=BGR' ! v4l2sink device=/dev/video10

3.Run the YOLO detection script

python webcam_yolo.py

Press 'q' to exit fullscreen mode.

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Third-Party Licenses

This project uses NRtred/yolo11n_object365, which is licensed under the MIT License. All third-party license details are stored in the Third-Party Licenses/ directory for reference.