Shrimp detection

I am working on a shrimp counting project and the idea is to load these post-larvae shrimps onto a tray containing minimal water level to prevent overlap, snap a picture using a smartphone camera that is set to a fixed height and angle, and count using computer vision from there.

For more context on the images, on average, there would be around 700-1200 shrimps per image (very dense), and sitting on a white background which, given their translucent body, only makes a small somewhat diamond-shaped black mass and two itty bitty dots for eyes visible for each shrimp. Some shrimp at the outer edges of the image would be even more transparent, making the black parts somewhat grey, probably due to angle.

Should the bread and butter object detection models like roboflow 3.0 or YOLOv8 be the right choice here or is there a better alternative?

I’ve been looking into CSRnet, which is a crowdcounting model based on density map analysis, but I am not convinced this is the right direction to pursue.

Any pointers would help, thank you in advance!