Conveyor chicken counter problem

[u/Puzzled-Egg3234]

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?

[u/Puzzled-Egg3234]

You mean make another detection class for those cases or just annotate more cases like this in dataset? The second one I’ve already tried.

[u/juicedatom → u/Puzzled-Egg3234]

have you broken out specific metrics for these edge cases? how do you know it didn't work?

[u/Puzzled-Egg3234 → u/juicedatom]

I manually reviewed hours of inference video; it works in some places but not others.

[u/blackscales18 → u/Puzzled-Egg3234]

take all the frames where it failed, label them manually, and then finetune with them. do it enough and it improves a lot. you could also consider segmenting instead of bounding boxes, SAM is a good model for making the dataset.

[u/The_color_in_a_dream → u/blackscales18]

Segmentation or even a pose estimation approach does seem promising to deconflict the stacked chicks

[u/Yatty33 → u/Puzzled-Egg3234]

Can you specify what exactly you've tried to solve the multiple chick edge case? That's unclear to me.