# Object detection and display
Source: [https://docs.qualcomm.com/doc/80-70015-50/topic/camera-detection-display.html](https://docs.qualcomm.com/doc/80-70015-50/topic/camera-detection-display.html)
The **gst-camera-detect-display.py** script uses a YOLO v8 TFLite model to detect
objects in the camera stream, draw bounding boxes around the identified objects, and then
display the results.
## Use cases
1. Ensure that you complete the [Prerequisites](https://docs.qualcomm.com/doc/80-70015-50/topic/python-sample-applications.html#python-sample-applications__section_gm5_s5j_bdc).
2. Run the object detection
script:
python3 /usr/bin/gst-camera-detect-display.pyCopy to clipboard
The following are the default file paths in the Python script:
MODEL_FILE = "/opt/data/YOLOv8-Detection-Quantized.tflite" Copy to clipboard
LABELS_FILE = "/opt/data/yolov8.labels"Copy to clipboard
## Expected output
Figure : Expected output for object detection application
## Pipeline flow
Figure : Pipeline for object detection and preview
| Process | Description |
| --- | --- |
| [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70015-50/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.