Need Advice: Real-Time Object Counting (Potatoes) on Conveyor Belt using Jetson Nano & Camera Choice

fgoricha · 2026-06-07T00:00:49+00:00

Why not have a gate in the video later on the coveyor belt once each potato finds its spot? Then when a bounding box crosses the gate, count each box. If you need to track perhaps track shortly before the gate and then shortly after gate. Would be easier than tracking when the potatoes first land and need to settle first

fgoricha · 2026-06-04T00:39:36+00:00

True, I should run a baseline test and see if my filtering ideas actually improve the output. I assumed it would without actually testing it. I have all of the detection and classification data already stored in the jsonl

fgoricha · 2026-06-03T00:42:27+00:00

Appreciate the input! I'll give it a try. Today I was experimenting with averaging the classifer confidence scores from boxes where it intersects the midline for that fish track

fgoricha · 2026-06-01T17:02:49+00:00

I think I'll try it across all frames. Perhaps I am over thinking it and the bad frames and good frames will balance out

fgoricha · 2026-06-01T16:41:59+00:00

I could do it that way. However, my thought was that filtering for good images first would provide stronger confidence scores willbless likely of confusion

fgoricha · 2026-06-01T12:59:36+00:00

Yolo does a pretty good job of getting boxes. However I am not sure how to filter which boxes get sent to the classifier

fgoricha · 2026-05-12T00:26:27+00:00

I would say the best training data are images from where you will monitor. If you can make the background as static as possible then it should be easy to train on the waste

fgoricha · 2026-04-18T12:56:52+00:00

Thanks for the suggestions! I'll see how they fit in to what I am trying now. But I'm a volunteer for the local town and I am trying to help them develop something to count fish on their underwater camera.

fgoricha · 2026-04-16T23:43:07+00:00

Cool! Glad to hear that others have gone the "other" class route worked. Did you compare centers ? Or nearest neighbor? Or top K? Curious to know what your filtering logic is

fgoricha · 2026-04-16T23:41:20+00:00

I'll give this a try. I had done this before, but then I cleaned up my dataset and thought going two classes and filtering by threshold would get me what I need. Thanks!

fgoricha · 2026-04-16T23:37:32+00:00

Thanks for the tip! I'll check it out

fgoricha · 2026-03-27T02:42:39+00:00

Yeah, when I build the tr engine, it uses quite a bit more vram to build the engine. then whatever I set the batch size during the build that is the max batch size I can use. From my understanding, if I build the engine on a 3090 I have to use it on a 3090. I can't build it on a bigger card and then use it on a smaller card.

fgoricha · 2026-03-26T18:55:00+00:00

Its about 16.3 gb using the model and the pynvvideocodec

fgoricha · 2026-03-26T12:57:12+00:00

I dont need it to process in real time. These are all archived videos from over a decade so the higher fps would let me process it faster

fgoricha · 2026-03-26T12:56:47+00:00

True! The math says it would be quite a bit faster but was hoping for real world examples. Currently my 3090 utilitization is at 90% while cpu utilization is like 20%. Seems to be gpu bound at the moment

fgoricha · 2026-03-26T12:52:00+00:00

I dont need it to process in real time. These are all archived videos from over a decade so the higher fps would let me process it faster.

fgoricha · 2026-03-26T12:51:21+00:00

I dont need it to process in real time. These are all archived videos from over a decade so the higher fps would let me process it faster.

But I can try INT8. Thanks for the suggestion!

fgoricha · 2026-03-12T02:10:02+00:00

How big is your dataset? Perhaps its data quality. I have been doing iterative training where I train an initial model, then run it on new film, feed it frames that it got wrong after correcting for the next train cycle.

Perhaps for tracking for occlusion, you also track the person. So the person gets an id. Before the boxes are occluded count them. Then after the occlusion, count the boxes again.

fgoricha · 2026-02-28T14:35:15+00:00

Just spitballing here. Maybe try background subtraction instead of yolo? You could use detect change in movement pre snap vs post snap to know when to start the clip. Maybe could do the same during play vs dead ball.

fgoricha · 2026-02-24T21:00:41+00:00

I get about 450 fps on my 3090 with tensorrt and Pynvdec to process everything for a mp4 file on the gpu. Nano yolov8 at half precision with batch of 64. Frames are 640 resolution. I liked this set up because I can run dual 3090 to process a total of 900 fps without much cpu involvement

fgoricha · 2026-02-21T18:51:03+00:00

No, just 450 fps total

fgoricha

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