Object detection with no identification

Hi everyone,

This will be an odd question as I am new in programming for image detection and classification. I am collecting data from an experiment using a jetson nano, a liquid going through a channel carrying objects for detection which need to be initially classify by an expert. I want to create a software to detect any potential object, creating a bounding box for objects of certain size. I have found that this is integrated into the detection models (by checking jetson inference documentation) but I have not found any tutorials on how to implement it as I am not familiar with the technical terms.

Any tutorial or tip on this topic would be really appreciated.
Thank you in advance for your time and support,
Jorge Silva

Hi @jorgersilvac, you can follow this tutorial for collecting + annotating your own data, training your own detection model, and deploying it on Jetson Nano:

Thank you for your response.
I read online and I found “Tracking Objects Using Contours” but most of the samples are done on cpu using opencv.
Does anyone know of something similar done on GPU ?

If you are talking about tracking, DeepStream does tracking on the GPU.

jetson-inference does object detection on the GPU, but not tracking.

Thank you for your time, I am a bit confused at the moment. I should explain better what I want to do, I am not quite sure how to implement it.

I am collecting data from an experiment setup using a camera and a jetson nano, a liquid going through a channel carrying objects for detection and counting.
I am not planning to do classification live, I am more concerned with accuracy rather than speed. I will use all the pixels available from the sensor as I believe this will improve accuracy of the classification, 4056 x 3040 pixels HQ PiCamera.

  1. I want to identify any potential object from the video stream and create a bounding box around the object. I read about selective search or using contours for this, I am not sure if this is the right approach.

  2. I want to extract only one image of each potential object using the bounding box, I think object tracking might be the right choice but not sure. The number of objects at any time has not exceed 5.

  3. I will classify the images to identify its class using the jetson-inference docker, move all objects of a class into a folder and count the number of objects.

I believe this summarizes what I want to do. I hope it is clear enough otherwise I will clarify any doubt you might have.
Any advice anyone could give me on how to tackle this task will be greatly appreciated.

Thank you for your time,

Hi Jorge, I can’t really comment on the specifics of your approach, but for tracking I would recommend looking into either DeepStream or OpenCV. If you train a DNN model then you can use DeepStream for object counting.

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