# Object detection and display with Neural Processing SDK Source: [https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-object-detection-and-display-with-mobilenet-v2-ssd.html](https://docs.qualcomm.com/doc/80-70014-50/topic/single-camera-stream-with-object-detection-and-display-with-mobilenet-v2-ssd.html) The use cases use a yolonas.dlc object detection model with Qualcomm Neural Processing SDK to identify an object from a camera stream, overlay or compose the bounding boxes over the detected objects, and then display the results. ## Variant 1: Use qtioverlay plugin to apply bounding box overlay Use the following command to execute the use case: setprop persist.overlay.use_c2d_blit 2Copy to clipboard gst-launch-1.0 -e \ qtiqmmfsrc name=camsrc ! video/x-raw\(memory:GBM\),format=NV12,width=1280,height=720,framerate=30/1,compression=ubwc ! queue ! tee name=split \ split. ! queue ! qtimetamux name=metamux ! queue ! qtioverlay ! queue ! waylandsink fullscreen=true sync=false \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/opt/yolonas.dlc layers="" ! queue ! qtimlvdetection threshold=51.0 results=10 module=yolo-nas labels=/opt/yolonas.labels ! text/x-raw ! queue ! metamux.Copy to clipboard To stop the use case, press CTRL + C. Figure : Pipeline for bounding box overlay ![](data:image/png;base64,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) The figure shows the flow of the use case execution: - Identify an object in scene from video stream coming through camera source. - Overlay bounding boxes over the detected objects using overlaylib. - Display the results on a local display. The table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70014-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:


| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


  3. Converts the video stream to a tensor stream on its source
    pad.

    The object detection model uses this tensor
    stream for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimlsnpe.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Executes the inference and produces tensor stream with the
    object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlvdetection](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimlvdetection.html) |

  1. Receives the inference tensors from the object detection
    model.


  2. Converts the inference tensors on its sinkpad into formats
    like video or text that the multimedia plugins can process
    later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.

    In
    this use case, qtimlvdetection does the following:


    1. Loads YOLO-NAS submodule.


    2. Produces results as structures of text.


    3. Sends them to sinkpad of qtimetamux.





| | [qtimetamux](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimetamux.html) |

  1. Receives video stream and text stream with bounding box
    results corresponding to the video stream on its
    sinkpads.


  2. Produces GST buffers with contents of video stream from its
    sink pad.


  3. Adds bounding boxes as GstVideoRegionOfInterest from data
    sinkpad to GST buffers meta (meta muxing) on its source
    pad.


| | [qtioverlay](https://docs.qualcomm.com/doc/80-70014-50/topic/qtioverlay.html) |

  1. Receives the multiplexed stream.


  2. Overlays the bounding boxes on the VideoFrame using CL.


  3. Produces GST buffers with overlays in its source pad.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-70014-50/topic/waylandsink.html) |

  1. Receives the video stream on its sinkpad.


  2. Submits the video stream to Weston.


  3. Weston renders the video stream and bounding boxes generated
    for the objects in that scene on a local display
    device.


| ## Variant 2: Use qtivcomposer to mix original frame with bounding box mask Use the following command to execute the use case: gst-launch-1.0 -e --gst-debug=2 \ qtiqmmfsrc name=camsrc ! video/x-raw\(memory:GBM\),format=NV12,width=1280,height=720,framerate=30/1,compression=ubwc ! queue ! tee name=split \ split. ! queue ! qtivcomposer name=mixer sink_1::dimensions="<1920,1080>" ! queue ! waylandsink fullscreen=true sync=false \ split. ! queue ! qtimlvconverter ! queue ! qtimlsnpe delegate=dsp model=/opt/yolonas.dlc layers="" ! queue ! qtimlvdetection threshold=51.0 results=10 module=yolo-nas labels=/opt/yolonas.labels ! video/x-raw,width=640,height=360 ! queue ! mixer.Copy to clipboard To stop the use case, press CTRL + C. Figure : Pipeline for bounding box mask with qtivcomposer ![](data:image/png;base64,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) The figure shows the flow of the use case execution: 1. Identifies object scenes in the scene from a video stream, which is coming through a camera source. 2. Composes the following using qtivcomposer: 1. Bounding boxes over objects detected. 2. Original video stream. 3. Display the results. The table provides the sequential processing stages of the pipeline execution: | Process | Description | | --- | --- | | [qtiqmmfsrc](https://docs.qualcomm.com/doc/80-70014-50/topic/qtiqmmfsrc.html) |

  1. Collects the video stream (source) and creates two copies of
    the source:

    1. One stream is sent to qtivcomposer plugin to retain
      the video stream.


    2. The other stream is sent to a ML inferencing
      pipeline.





| | **Preprocessing** | **Preprocessing** | | [qtimlvconverter](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimlvconverter.html) |

  1. Receives the video stream on its sink pad.


  2. Performs preprocessing:


  3. Converts the video stream to a tensor stream on its source
    pad.

    The object detection model uses this tensor
    stream for inferencing.




| | **Inferencing** | **Inferencing** | | [qtimlsnpe](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimlsnpe.html) |

  1. Loads the object detection model.


  2. Modifies the graph for the chosen delegate.


  3. Receives the tensor stream on its sinkpad.


  4. Executes the inference and produces tensor stream with the
    object detection results on its source pad.


| | **Postprocessing** | **Postprocessing** | | [qtimlvdetection](https://docs.qualcomm.com/doc/80-70014-50/topic/qtimlvdetection.html) |

  1. Receives the inference tensors from the objection detection
    model.


  2. Converts the inference tensors on its sinkpad into formats
    like video or text that the multimedia plugins can process
    later.


  3. Applies the threshold to the chosen number of results.


  4. Loads the corresponding modules for detection models.

    In
    this use case, qtimlvdetection does the following:


    1. Loads the YOLO-NAS submodule.


    2. Produces video frames with only bounding boxes that
      can be overlaid on objects.


    3. Sends them to sinkpad of qtivcomposer.





| | [qtivcomposer](https://docs.qualcomm.com/doc/80-70014-50/topic/qtivcomposer.html) |

  1. Receives the original video stream and video stream with
    bounding boxes on its sinkpads.


  2. On its sourcepads, produces content that is composed of
    video streams processed from its sinkpads.


| | **Output** | **Output** | | [Waylandsink](https://docs.qualcomm.com/doc/80-70014-50/topic/waylandsink.html) |

  1. Receives the video stream on its sinkpad.


  2. Submits the video stream to Weston.


  3. Weston displays the following on a local display device:

    1. The video stream is captured from the camera.


    2. The bounding boxes are drawn over the allowed number
      of objects identified in that scene.





| **Parent Topic:** [Qualcomm Neural Processing SDK use cases](https://docs.qualcomm.com/doc/80-70014-50/topic/qualcomm-neural-processing-sdk-use-cases.html) Last Published: Oct 27, 2025 [Previous Topic Image classification and encode with Neural Processing SDK](https://docs.qualcomm.com/bundle/publicresource/80-70014-50/topics/single-camera-stream-with-image-classification-and-encode-with-mobilenet-v1.md) [Next Topic Object detection and encode with Neural Processing SDK](https://docs.qualcomm.com/bundle/publicresource/80-70014-50/topics/single-camera-stream-with-object-detection-and-encode-with-mobilenet-v2-ssd.md)