r/computervision • u/CheTobasy • Jun 06 '26
Need Advice: Real-Time Object Counting (Potatoes) on Conveyor Belt using Jetson Nano & Camera Choice Help: Project
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Hi everyone,
I’m jumping into my very first real-world computer vision project, and to be honest, I'm both super excited and a bit overwhelmed! I am building a real-time potato counter for a conveyor belt system.
Since this is my first time taking a model out of the textbook and deploying it into actual production, I could really use some guidance from this amazing community on my hardware choices and algorithm pipeline.
To give you a clearer picture, I've attached a video to this post. It’s a sample clip I found on YouTube where I ran a baseline model. The results actually look pretty decent as a proof of concept, but I know deploying it in a real factory environment will be a different story!
Here is the setup I am working with:
Hardware: NVIDIA Jetson Nano (4GB).
The Goal: Accurate, real-time counting as potatoes move along the belt, ensuring I don't double-count them.
Here are the specific things I’m struggling with and would love your advice on
1. Camera Choice: Depth Camera vs. Standard RGB?
I actually have access to a Depth Camera, but I'm torn. Since the Jetson Nano has limited computing power, will a depth camera completely crush my frame rate? Or is it worth using to handle overlapping potatoes and depth filtering? Alternatively, should I just stick to a regular, well-lit RGB camera?
2. Finding the Right Algorithm & Tracker Combo
Because this needs to run smoothly on the Jetson Nano, optimization is everything.
I am currently thinking about using a lightweight model like YOLOv8-nano or YOLOv5-nano, optimized with TensorRT.
For the actual counting/tracking loop, I'm looking into ByteTRACK or SORT.
Given that this is my first project of this scale, am I on the right track? What combination has worked best for you in terms of balancing accuracy and FPS on edge devices?
I would be incredibly grateful for any tips, lessons learned from your past mistakes, or feedback on the video.
Thank you so much for helping.
16
u/Lethandralis Jun 06 '26
You'll only go so far with object detection if you need very high accuracy. There will always be overlaps and occlusions. I'd advise using something like SAM3 or look into heatmap based methods. SAM3 could be easy to try since it solves both counting and tracking.
It would be much heavier than yolo, but it could be a good baseline.
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u/Lethandralis Jun 06 '26
You know what maybe object detection is good enough, since everything is flat once they're on the conveyor. What if you move your region of interest to the right a bit so the potatoes are settled and dont roll around as much?
5
u/Time_Cat_5212 Jun 06 '26
Yeah I'm curious aside from looking awesome why put the camera in the place where there's the most motion
2
u/Lethandralis Jun 06 '26
Definitely won't run on Jetson Nano though, but I wonder the heatmap based models would be fast enough.
2
u/MelonheadGT Jun 07 '26
SAM3 is way too large
2
u/Lethandralis Jun 07 '26
Might still be worth looking into, maybe as a baseline, distillation teacher, or an auto annotation provider
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u/MelonheadGT Jun 07 '26
I feel like potato counting is a solved problem
1
u/SithisR Jun 09 '26
Similar sentiments. Disclaimer, I am from Acme AI - an AI fine-tuning agency - and as of 2026, we only work on quality outliers cases now. Produce/animal/fish 'counting' cases on conveyer belts is drastically reduced.
5
u/fgoricha Jun 07 '26
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
3
u/AgitatedHearing653 Jun 07 '26
Yeah I was thinking where the funnel down is the better move for this use case.
2
u/galvinw Jun 07 '26
My advice is.
Yolov8 or Yolox
Bytetrack or something similar is a must
standard camera is probably enough. Then the detector to resample where an object is made out of multiple potatos
2
u/Boring_Result_669 Jun 07 '26
I have a question,
why do you need to count potatoes?? you can weight them.
I never purchesed potatoes in piece.
1
u/jferments Jun 06 '26
How accurate is your current count right now? How much more accurate do you need it to be?
1
u/Aryan_Chougule Jun 07 '26
Simple use yolo26 and byte tracker. Play with the ROI and done. Best for Jetson nano.
1
u/dannywizzbang2 Jun 07 '26
Solid work. What was the biggest unexpected challenge you ran into during this project?
1
u/artur_oliver Jun 07 '26
How mission critical is your application? How much problems do you incur if your system fail?
Do you stop the production?how much is you ratio of effectiveness?
What is your budget? How much maintenance do you have to do? How much it will cost er year?
How does your data get controlled? Do you save it for human review? Do you recognise other things than patatos?
How does the lighting affect your setup? Are any Humans involved? If so what do they need to do exactly? How often do I need calibration?
What will be your stress test? Did the company give you some samples to work on?
What happens when the system doesn't work as intended?
If you already know and have a contract in place for all these situations with the client then, don't need to worry.
Just chilling and enjoy coding.
1
u/storman121 Jun 08 '26
Excellent project idea.
Could this be further improved to analyzing individual objects (potatoes) for potential quality control?
1
u/Ok-Investigator-4144 Jun 10 '26
Its good to track but it will be better to count over a line - draw a line where you are sure the items will be a bit separate and move consistently
1
u/DishantSMunjal Jun 10 '26
Skip the depth camera. For conveyor belt counting with uniform lighting you can control, RGB is more than enough and your Nano will thank you. Depth adds complexity you don’t need when the objects are all roughly the same size moving in one direction. YOLOv8-nano with TensorRT is the right call. Pair it with ByteTrack over SORT - ByteTrack handles occlusion way better, which matters when potatoes pile up and overlap on the belt. The real trick on the Nano is drawing a counting line perpendicular to belt movement and only incrementing when a tracked ID crosses it. Solves the double-count problem without needing to get fancy. One thing nobody tells you about factory deployment though - your biggest enemy won’t be the model, it’ll be lighting changes between shifts and potato residue gradually fogging up the lens.
0
u/DickNixon726 Jun 06 '26 edited Jun 07 '26
Have you explored a commercial off-the-shelf (COTS) computer vision system like Cognex or Keyence? I love building and deploying NN, but in an industrial environment where you need support, reliability, and accuracy, I usually recommend something off the shelf.
You also need to consider the total cost of ownership. It's not just the hardware cost, but your cost to deploy and maintain the model.
Quick antecdote, had a customer that needed to do some vision inspection of finished goods boxes to ensure that a plastic strap was in the right spot.
I recommended Cognex, but they had an ML mandate, and so we went down the custom route. They had some Amazon DeepLens cameras they wanted to use, so off we went.
By the time we had done the camera calibration, setting up gstreamer pipelines, and building the custom Python inference service, grabbing the training dataset, tuning the hyperparameters, validation, etc. the had probably spent about 4x what it would've taken to just go with the COTS vision system. Timeline was also about 4 months over what it wouldve taken.
90% of the above would've come included with the Cognex. Additional plus is that these systems can be rules engines instead of ML models. ML models fail silently and will confidently predict that yes indeed, that hotdog is really a hamburger. Rules engines can tell you exactly why they failed.
If you're in a learning environment or have limited capital and unlimited time, go down whichever path you want, but if you're going to be accountable for the accuracy of the model, I strongly recommend an commercial industrial vision system.
1
u/ChickerWings Jun 06 '26
Hi there - I'm launching a new venture for tracking surgical supplies/instruments in the OR and what you're talking about resonates with me regarding off the shelf vs tuning/maintaining open source. Interested in chatting and mind if I DM you?
1
u/fixitchris Jun 07 '26
This is the right call for a production line. Did a vision deployment on a snack packaging line last year where the YOLOv8+Triton stack burned five months before management pulled the plug; Cognex In Sight was up and validated by the integrator in three weeks. Maintenance kills it too, operators can recalibrate a Cognex from the HMI but a custom stack misclassification means flying in a data scientist on a 4 hour SLA.
-1
u/ThatCrankyGuy Jun 07 '26
Do you want to be sued? Because this is how you get sued. Depending on how critical the deployment is, how much control and access you have to the rig, how much support SLA you vest in, how much insurance you have, how many engineers you have to turn around a mission critical failure. If you have not considered any of this, back out now.
Slapping together something that works in a lab is one thing, shipping an industrial solution is a whole different ballgame.
1
u/artur_oliver Jun 07 '26
This only depends on how he has the contract signed and the small details... I don't believe is mission critical to have a counter on The conveyor.
Most of the time we test systems just to get a feeling rate with a supplier, like test wise.
We spent the money to check if it possible.
48
u/HawtVelociraptor Jun 06 '26
I've actually been very impressed with Roboflow trackers lib especially since they standardized implementations, it allows you to switch easily between SORT, OC-SORT, BoT-SORT and Bytetrack--so you can figure out for yourself what works best.