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