# Object detection and display
Source: [https://docs.qualcomm.com/doc/80-70020-50/topic/camera-detection-display.html](https://docs.qualcomm.com/doc/80-70020-50/topic/camera-detection-display.html)
The **gst-ai-object-detection.py** application allows you to detect objects in the
camera stream or a file stream and display the results or save the output to a
file.
Note: For Ubuntu Server, this sample application isn't supported on
Dragonwing IQ-8275.
Figure : Pipeline for object detection and preview
For information about the plugins used in this pipeline, see [Pipeline flow](https://docs.qualcomm.com/doc/80-70020-50/topic/camera-detection-display.html#camera-detection-display__section_jbz_cbk_bdc).
## Run the application on the target device
1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70020-50/topic/prerequisites-for-python-sample-applications.html).
2. Export the YOLOv8 model and update the model name in the commands with the
appropriate model name as downloaded through AI Hub.
3. Run any of the following use cases:
- Display with the primary and secondary cameras respectively:
- gst-ai-object-detection.py -c 0 -f 2 -m /etc/models/yolov8_det_quantized.tflite -l /etc/labels/yolov8.labels -ml "yolov8" -k "YOLOv8,q-offsets=<-107.0, -128.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>"Copy to clipboard
- gst-ai-object-detection.py -c 1 -f 2 -m /etc/models/yolov8_det_quantized.tflite -l /etc/labels/yolov8.labels -ml "yolov8" -k "YOLOv8,q-offsets=<-107.0, -128.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>"Copy to clipboard
- Display with input from a video
file:
gst-ai-object-detection.py -s /etc/media/video.mp4 -f 2 -m /etc/models/yolov8_det_quantized.tflite -l /etc/labels/yolov8.labels -ml "yolov8" -k "YOLOv8,q-offsets=<-107.0, -128.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>"Copy to clipboard
- YOLO-NAS with
LiteRT:
gst-ai-object-detection.py -f 2 -m /etc/models/yolonas.tflite -l /etc/labels/yolonas.labels -ml "yolo-nas" -k "YOLO-NAS,q-offsets=<37.0, 0.0, 0.0>,q-scales=<3.416602611541748, 0.00390625, 1.0>"Copy to clipboard
- YOLOv8 with
LiteRT:
gst-ai-object-detection.py -f 2 -m /etc/models/yolov8.tflite -l /etc/labels/yolov8.labels -ml "yolov8" -k "YOLOv8,q-offsets=<-107.0, -128.0, 0.0>,q-scales=<3.093529462814331, 0.00390625, 1.0>"Copy to clipboard
Note: Update the `q_offset` and
`q_scale` constants of the quantized LiteRT model in the
command. For instructions, see [get model constants](https://docs.qualcomm.com/bundle/publicresource/topics/80-90441-15/integrate-ai-hub-models.html#get-model-constants).
Table : Sample model and label files for object detection and display python
application
| Runtime | Models | Labels |
| :--- | :--- | :--- |
| LiteRT | yolov8_det_quantized.tflite | yolonas.labels |
| Qualcomm Neural Processing SDK | yolonas.dlc | yolonas.labels |
4. To display the available help options, run the following
command:
gst-ai-object-detection.py -hCopy to clipboard
## Expected output
Figure : Expected output for object detection and display application–Preview
## Pipeline flow
| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70020-50/topic/qtiqmmfsrc.html) |
Collects the video stream (source) and creates two copies of the source:
One stream is sent to the qtimetamux plugin to retain the video stream.
The other stream is sent to an ML inferencing pipeline.
|
| filesrc | Reads the video data. |
| **Preprocessing** | **Preprocessing** |
| [qtimlvconverter](https://docs.qualcomm.com/doc/80-70020-50/topic/qtimlvconverter.html) |
Receives the video stream on its sinkpad.
Performs preprocessing:
Color conversion
Scaling down/up
Normalization on the stream data when the model expects the floating point values as input
Converts the video stream to a tensor stream on its source pad.
The object detection model uses this tensor stream for inferencing.
Weston renders the video stream and bounding boxes generated for the objects in that scene on a local display device.
|
| Filesink | Writes the video to a file. |
## Related information
[Object detection](https://docs.qualcomm.com/doc/80-70020-50/topic/gst-ai-object-detection.html)
**Parent Topic:** [Python applications](https://docs.qualcomm.com/doc/80-70020-50/topic/python-sample-applications.html)
Last Published: Jan 30, 2026
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