Vehicle Detection Model used

I am new to deep learning and i have just started jetson-inference. Currently i am planning to detect only vehicle in real time and i would like to ask if it would be better for me to train my own model or to use the pre-trained models(where there are many types of objects classified).

Also, would doing some pre-processing on the frames be good(ie background differencing) for the object detection model or should i just stick to the original frames? In other words, is it worth doing pre-processing or can the gpu support the object detection process at a real time rate (~25fps perhaps).


We have a TrafficCamNet model in our Deepstream SDK.
You can check if the performance can meet your requirement first.

This model can reach 656 fps on Xavier so it’s expected that it can meet 25 fps on TX2.

do i need to configure the model? Or can i simply plug it in like how i do in detectnet for jetson-inference? I want to try it without the Transfer Learning first if possible

Currently i have tried the models like ssd, googlenet but they are not very good at capturing moving objects in traffic flow. The accuracy isnt that good


Have you maximized the device performance first?

$ sudo nvpmodel -m 0
$ sudo jetson_clocks


I think you misunderstood, the fps is around for the non-custom trained model(90 classes), the fps is around14. I believe if i custom trained it to only detect the classes i require(~5), the throughput should go up. My main concern is with false detection


The model can be inferenced with deepstream sample directly:

And the accuracy is roughly 83% which mentioned our TLT page:

It’s recommended to give it a try to see if this can meet your requirement.

I have tested the sample and the detection rate is good on the sources/streams i am using. However, how do i deploy this model in my own program? I don’t really get the documents provided on say parameter extractions.


You can use it through the deepstream SDK.
Here are some example for Deepstream for your reference:

C++ : /opt/nvidia/deepstream/deepstream-5.0/sources/apps/sample_apps/
Python :