u/Puzzled-Egg3234

First of all, thank you to everyone who responded in the previous post. I haven't read all the replies yet, but many of the solutions seem interesting. I was able to find a more informative and higher‑quality video that better reflects the current state of the project.

https://www.reddit.com/r/computervision/s/meFAVvvFQo

Following up on the discussion from the previous post, I'm attaching the current state of affairs. The video was taken with good industrial lighting, and the global‑shutter camera was set to an exposure of 500. In this particular video, the counter showed 100%. However, in other counts we got varying ranges – 98–99%, which, at industrial volumes, leads to significant absolute losses.

The main issues with the current version are:

1. Loss of detection right within the detection zone;

2. Constant changes in the shape/size of the bounding box within the detection zone, causing the tracker to lose track and assign different IDs to the same object;

3. Occlusions and merging of chicks – several chicks form a single object by merging and partially overlapping each other. Increasing the dataset no longer solves this problem; the latest version had over 5,000 frames with plenty of such cases, and yet reviewing new videos showed that the issue is not fully resolved – there are still cases where multiple chicks are counted as one.

Counting these cases geometrically is also difficult – chicks of different breeds and ages can have different sizes, and on top of that, spreading their wings and legs changes the area of the detected box. There are cases where we hit the desired 99.8% range thanks to a combination of missed detections and false positives, but over a long run the error accumulates and we fall out of the range.

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?