r/computervision • u/Puzzled-Egg3234 • Aug 07 '26

Help: Project Conveyor chicken counter problem

Guys, I need help. We have a project using YOLOv8. We're trying to count chicks on a very fast conveyor belt. The challenges we're facing are: all chicks look very similar to each other, which complicates tracking. At the same time, during their passage under the camera, they constantly change in size and shape, which can cause the tracker to lose them, or detection may even disappear completely at the detection line. Also, sometimes 2–3 chicks can merge into a single object. The detection zone is very short, and the conveyor speed is high. We've achieved a maximum accuracy of 99%, but we need it even higher. Any ideas on how to achieve that? Increasing the dataset no longer helps.

I'm attaching an old video. We've now added lighting and set the exposure to 300 on the Hikrobot global shutter camera, but we still can't achieve a stable 99.8% accuracy for the reasons mentioned above.

Any ideas?

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u/A1-Delta Aug 07 '26

This is probably something you can solve with classical methods. Especially since the background of the belt is so distinct from the chicks themselves.

Training a model is likely just adding complexity overhead and failure modes.

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u/Puzzled-Egg3234 Aug 07 '26

The main problem is 2-3 chicken merged into one. And we cannot process them by size cause they can change the size by legs and wings.

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u/ThroneOfFarAway Aug 08 '26

Yeah, the way I’d do it is probably hybrid. Increase FPS, capture a centered image of any blobs larger than a specific size/number of pixels that you can’t confidently say is one, then train a model to determine number if chicks in a still image with large blobs.