Object Detection

In this example, the pipeline systematically analyzes each frame of a video stream to identify and localize multiple objects — such as people, vehicles, or other entities — within each frame. For each detected object, the pipeline provides bounding boxes and confidence scores. This example uses the YOLOX model from Qualcomm AI Hub.

The detection pipeline is structurally identical to the classification pipeline, with two key differences:

The inference plugin is configured with a detection model (YOLOX) instead of a classification model
The qtimlpostprocess plugin uses the yolov8 module with a higher result count to capture multiple detections per frame

Run example on device

Download Required Files

File	Download	Save as
YOLOX W8A8 model	Qualcomm AI Hub — YOLOX	`yolox_quantized.tflite`
Detection labels	coco.txt	`coco.txt`
Sample video	Input video	`ai_demo_sample.mp4`

If any downloaded file is a .zip archive, extract it on your host machine before copying: unzip filename.zip

Copy files to device

Create the required directories and transfer the downloaded files to your device.

# Replace $HOME to the appropriate device path before running the commands.
# For QLI:    /root
# For Ubuntu: /home/ubuntu
# Modify this based on your platform and ensure files are copied to the correct location on the device.
# Run from your host machine — replace <user> and <device-ip>
ssh <user>@<device-ip> "mkdir -p $HOME/{models,labels,media,media/output}"
scp yolox_quantized.tflite  <user>@<device-ip>:$HOME/models/
scp coco.txt                 <user>@<device-ip>:$HOME/labels/
scp ai_demo_sample.mp4                <user>@<device-ip>:$HOME/media/

Connect to device

ssh <user>@<device-ip>

Set environment variables

export MODEL_NAME=yolox_quantized.tflite
export LABELS_NAME=coco.txt
export SRC_VIDEO_NAME=ai_demo_sample.mp4
export VIDEO_SOURCE="filesrc location=$HOME/media/$SRC_VIDEO_NAME ! qtdemux ! h264parse ! v4l2h264dec capture-io-mode=4 output-io-mode=4 ! video/x-raw,format=NV12"

Run example on device

GStreamer Command line
GStreamer Python application
GStreamer native application

gst-launch-1.0 $VIDEO_SOURCE ! \
  tee name=t \
  t. ! qtimlvconverter name=preprocess ! queue ! \
       qtimltflite name=inference delegate=external \
         external-delegate-path=libQnnTFLiteDelegate.so \
         external-delegate-options="QNNExternalDelegate,backend_type=htp;" \
         model=$HOME/models/$MODEL_NAME ! queue ! \
       qtimlpostprocess name=postprocess results=8 module=yolov8 \
         labels=$HOME/labels/$LABELS_NAME settings='{"confidence": 51.0}' bbox-stabilization=true ! \
       text/x-raw ! metamux. \
  t. ! qtimetamux name=metamux ! qtivoverlay ! waylandsink sync=true fullscreen=true

Python source code: gst-ai-video-detection.py

Run:

python3 gst-ai-video-detection.py -s "$VIDEO_SOURCE" -o display

Application source code: gst-ai-video-detection
Build your application:
- Yocto
- Ubuntu
Steps to build custom application
Steps to build custom application

Run:

gst-ai-video-detection -s "$VIDEO_SOURCE" -o display

Expected output

Detection results are visually overlaid on each video frame — bounding boxes and class labels are rendered on top of the original image in real time.

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Run example on device

Expected output

​Run example on device

​Expected output

Run example on device

Expected output